Open Access. Powered by Scholars. Published by Universities.®
- Discipline
- Publication Year
- Publication
-
- Electrical and Computer Engineering Faculty Research & Creative Works (43)
- Engineering Management and Systems Engineering Faculty Research & Creative Works (10)
- Mechanical and Aerospace Engineering Faculty Research & Creative Works (9)
- Chemical and Biochemical Engineering Faculty Research & Creative Works (3)
- Computer Science Faculty Research & Creative Works (2)
Articles 1 - 30 of 69
Full-Text Articles in Entire DC Network
Nsf Career: Scalable Learning And Adaptation With Intelligent Techniques And Neural Networks For Reconfiguration And Survivability Of Complex Systems, Ganesh K. Venayagamoorthy
Nsf Career: Scalable Learning And Adaptation With Intelligent Techniques And Neural Networks For Reconfiguration And Survivability Of Complex Systems, Ganesh K. Venayagamoorthy
Electrical and Computer Engineering Faculty Research & Creative Works
The NSF CAREER program is a premier program that emphasizes the importance the foundation places on the early development of academic careers solely dedicated to stimulating the discovery process in which the excitement of research enriched by inspired teaching and enthusiastic learning. This paper describes the research and education experiences gained by the principal investigator and his research collaborators and students as a result of a NSF CAREER proposal been awarded by the power, control and adaptive networks (PCAN) program of the electrical, communications and cyber systems division, effective June 1, 2004. In addition, suggestions on writing a winning NSF …
Optimal Control Of A Photovoltaic Solar Energy System With Adaptive Critics, Richard L. Welch, Ganesh K. Venayagamoorthy
Optimal Control Of A Photovoltaic Solar Energy System With Adaptive Critics, Richard L. Welch, Ganesh K. Venayagamoorthy
Electrical and Computer Engineering Faculty Research & Creative Works
This paper presents an optimal energy control scheme for a grid independent photovoltaic (PV) solar system consisting of a PV array, battery energy storage, and time varying loads (a small critical load and a larger variable non-critical load). The optimal controller design is based on a class of adaptive critic designs (ACDs) called the action dependant heuristic dynamic programming (ADHDP). The ADHDP class of ACDs uses two neural networks, an "action" network (which actually dispenses the control signals) and a "critic" network (which critics the action network performance). An optimal control policy is evolved by the action network over a …
Online Reinforcement Learning-Based Neural Network Controller Design For Affine Nonlinear Discrete-Time Systems, Qinmin Yang, Jagannathan Sarangapani
Online Reinforcement Learning-Based Neural Network Controller Design For Affine Nonlinear Discrete-Time Systems, Qinmin Yang, Jagannathan Sarangapani
Electrical and Computer Engineering Faculty Research & Creative Works
In this paper, a novel reinforcement learning neural network (NN)-based controller, referred to adaptive critic controller, is proposed for general multi-input and multi- output affine unknown nonlinear discrete-time systems in the presence of bounded disturbances. Adaptive critic designs consist of two entities, an action network that produces optimal solution and a critic that evaluates the performance of the action network. The critic is termed adaptive as it adapts itself to output the optimal cost-to-go function and the action network is adapted simultaneously based on the information from the critic. In our online learning method, one NN is designated as the …
Application Of Neural Networks For Data Modeling Of Power Systems With Time Varying Nonlinear Loads, Joy Mazumdar, Ganesh K. Venayagamoorthy, Ronald G. Harley
Application Of Neural Networks For Data Modeling Of Power Systems With Time Varying Nonlinear Loads, Joy Mazumdar, Ganesh K. Venayagamoorthy, Ronald G. Harley
Electrical and Computer Engineering Faculty Research & Creative Works
Nowadays power distribution systems typically operate with nonsinusoidal voltages and currents. Harmonic currents from nonlinear loads propagate through the system and cause harmonic pollution. The premise of IEEE 519 is that there exists a shared responsibility between utilities and customers regarding harmonic control. Maintaining reasonable levels of harmonic voltage distortion depends upon customers limiting their harmonic current injections and utilities controlling the system impedance characteristics. Measurements of current taken at the point of common coupling (PCC) to a customer are expected to determine whether the customer is in compliance with IEEE 519. These measurements yield the combination of nonlinear load …
Comparison Of Nonuniform Optimal Quantizer Designs For Speech Coding With Adaptive Critics And Particle Swarm, Ganesh K. Venayagamoorthy, Wenwei Zha
Comparison Of Nonuniform Optimal Quantizer Designs For Speech Coding With Adaptive Critics And Particle Swarm, Ganesh K. Venayagamoorthy, Wenwei Zha
Electrical and Computer Engineering Faculty Research & Creative Works
This paper presents the design of a companding nonuniform optimal scalar quantizer for speech coding. The quantizer is designed using two neural networks to perform the nonlinear transformation. These neural networks are used in the front and back ends of a uniform quantizer. Two approaches are presented in this paper namely adaptive critic designs and particle swarm optimization, aiming to maximize the signal-to-noise ratio. The comparison of these optimal quantizer designs over a bit-rate range of 3-6 is presented. The perceptual quality of the coding is evaluated by the International Telecommunication Union's Perceptual Evaluation of Speech Quality standard
Predicting Load Harmonics In Three Phase Systems Using Neural Networks, Joy Mazumdar, Frank C. Lambert, Ganesh K. Venayagamoorthy, Ronald G. Harley
Predicting Load Harmonics In Three Phase Systems Using Neural Networks, Joy Mazumdar, Frank C. Lambert, Ganesh K. Venayagamoorthy, Ronald G. Harley
Electrical and Computer Engineering Faculty Research & Creative Works
This paper proposes a artificial neural network (ANN) based method for the problem of measuring the actual harmonic current injected into a power system network by three phase nonlinear loads without disconnecting any loads from the network. The ANN directly estimates or identifies the nonlinear admittance (or impedance) of the load by using the measured values of voltage and current waveforms. The output of this ANN is a waveform of the current that the load would have injected into the network if the load had been supplied from a sinusoidal voltage source and is therefore a direct measure of load …
Density Estimation Using A Generalized Neuron, R. Kiran, Ganesh K. Venayagamoorthy, M. Palaniswami
Density Estimation Using A Generalized Neuron, R. Kiran, Ganesh K. Venayagamoorthy, M. Palaniswami
Electrical and Computer Engineering Faculty Research & Creative Works
Neural networks have been shown to be useful tools for density estimation. However, the training of neural network structures is time consuming and requires fast processors for practical applications. A new method with a generalized neuron (GN) for density estimation is presented in this paper. The GN is trained with the particle swarm optimization algorithm which is known to have fast convergence than the standard backpropagation algorithm. Results are presented to show that the GN can estimate the density functions for distribution functions with different means and variances. This density estimation method can also be applied to the multi-sensor data …
Permutation Coding Technique For Image Recognition Systems, Ernst M. Kussul, Tatiana N. Baidyk, Donald C. Wunsch, Oleksandr Makeyev, Anabel Martin
Permutation Coding Technique For Image Recognition Systems, Ernst M. Kussul, Tatiana N. Baidyk, Donald C. Wunsch, Oleksandr Makeyev, Anabel Martin
Electrical and Computer Engineering Faculty Research & Creative Works
A feature extractor and neural classifier for image recognition systems are proposed. The proposed feature extractor is based on the concept of random local descriptors (RLDs). It is followed by the encoder that is based on the permutation coding technique that allows to take into account not only detected features but also the position of each feature on the image and to make the recognition process invariant to small displacements. The combination of RLDs and permutation coding permits us to obtain a sufficiently general description of the image to be recognized. The code generated by the encoder is used as …
Gene Expression Data For Dlbcl Cancer Survival Prediction With A Combination Of Machine Learning Technologies, Rui Xu, Xindi Cai, Donald C. Wunsch
Gene Expression Data For Dlbcl Cancer Survival Prediction With A Combination Of Machine Learning Technologies, Rui Xu, Xindi Cai, Donald C. Wunsch
Electrical and Computer Engineering Faculty Research & Creative Works
Gene expression profiles have become an important and promising way for cancer prognosis and treatment. In addition to their application in cancer class prediction and discovery, gene expression data can be used for the prediction of patient survival. Here, we use particle swarm optimization (PSO) to address one of the major challenges in gene expression data analysis, the curse of dimensionality, in order to discriminate high risk patients from low risk patients. A discrete binary version of PSO is used for gene selection and dimensionality reduction, and a probabilistic neural network (PNN) is implemented as the classifier. The experimental results …
Neural Network Approach For Obstacle Avoidance In 3-D Environments For Uavs, Vivek Yadav, Xiaohua Wang, S. N. Balakrishnan
Neural Network Approach For Obstacle Avoidance In 3-D Environments For Uavs, Vivek Yadav, Xiaohua Wang, S. N. Balakrishnan
Mechanical and Aerospace Engineering Faculty Research & Creative Works
In this paper a controller design is proposed to get obstacle free trajectories in a three dimensional urban environment for unmanned air vehicles (UAVs). The controller has a two-layer architecture. In the upper layer, vision-inspired Grossberg neural network is proposed to get the shortest distance paths. In the bottom layer, a model predictive control (MPC) based controller is used to obtain dynamically feasible trajectories. Simulation results are presented for to demonstrate the potential of the approach.
Neural Network Detection And Identification Of Electronic Devices Based On Their Unintended Emissions, Haixiao Weng, Xiaopeng Dong, Xiao Hu, Daryl G. Beetner, Todd H. Hubing, Donald C. Wunsch
Neural Network Detection And Identification Of Electronic Devices Based On Their Unintended Emissions, Haixiao Weng, Xiaopeng Dong, Xiao Hu, Daryl G. Beetner, Todd H. Hubing, Donald C. Wunsch
Electrical and Computer Engineering Faculty Research & Creative Works
Electromagnetic emissions were measured from several radio receivers to demonstrate the possibility of detecting and identifying these devices based on their unintended emissions. Radiated fields from the different radio receivers have unique characteristics that can be used to identify these devices by analyzing time-frequency plots of measured radiation. A neural network was also developed for automated device detection.
Neural Networks Based Non-Uniform Scalar Quantizer Design With Particle Swarm Optimization, Wenwei Zha, Ganesh K. Venayagamoorthy
Neural Networks Based Non-Uniform Scalar Quantizer Design With Particle Swarm Optimization, Wenwei Zha, Ganesh K. Venayagamoorthy
Electrical and Computer Engineering Faculty Research & Creative Works
Quantization is a crucial link in the process of digital speech communication. Non-uniform quantizer such as the logarithm quantizers are commonly used in practice. In this paper, a companding non-uniform quantizer is designed using two neural networks to perform the nonlinear transformation. Particle swarm optimization is applied to find the weights of neural networks such that the signal to noise ratio (SNR) is maximized. Simulation results on different speech samples are presented and the proposed quantizer design is compared with the logarithm quantizer for bit rates ranging from 3 to 8.
Image Recognition Systems With Permutative Coding, Ernst M. Kussul, Donald C. Wunsch, Tatiana N. Baidyk
Image Recognition Systems With Permutative Coding, Ernst M. Kussul, Donald C. Wunsch, Tatiana N. Baidyk
Electrical and Computer Engineering Faculty Research & Creative Works
A feature extractor and neural classifier for image recognition system are proposed. They are based on the permutative coding technique which continues our investigations on neural networks. It permits us to obtain sufficiently general description of the image to be recognized. Different types of images were used to test the proposed image recognition system. It was tested on the handwritten digit recognition problem, the face recognition problem and the shape of microobjects recognition problem. The results of testing are very promising. The error rate for the MNIST database is 0.44% and for the ORL database is 0.1%.
Wide Area Power System Protection Using A Learning Vector Quantization Network, Ganesh K. Venayagamoorthy, Mahyar Zarghami
Wide Area Power System Protection Using A Learning Vector Quantization Network, Ganesh K. Venayagamoorthy, Mahyar Zarghami
Electrical and Computer Engineering Faculty Research & Creative Works
This paper presents a wide area monitoring and protection technique based on a learning vector quantization (LVQ) neural network. Phasor measurements of the power network buses are monitored continuously by a LVQ network in order to alert the control room operators of possible faults. The proposed scheme could be used in a wide area monitored network to provide remedial action when primary local protection schemes for transmission lines fail to function. This technique could also be extended to the actuation of the secondary protection schemes, hence, preserving the integrity of the power network especially when the faults are spreading over …
A Neural Network Based Optimal Wide Area Control Scheme For A Power System, Ganesh K. Venayagamoorthy, Swakshar Ray
A Neural Network Based Optimal Wide Area Control Scheme For A Power System, Ganesh K. Venayagamoorthy, Swakshar Ray
Electrical and Computer Engineering Faculty Research & Creative Works
With deregulation of the power industry, many tie lines between control areas are driven to operate near their maximum capacity, especially those serving heavy load centers. Wide area control systems (WACSs) using wide-area or global signals can provide remote auxiliary control signals to local controllers such as automatic voltage regulators, power system stabilizers, etc to damp out inter-area oscillations. This paper presents the design and the DSP implementation of a nonlinear optimal wide area controller based on adaptive critic designs and neural networks for a power system on the real-time digital simulator (RTDS©). The performance of the WACS as a …
Comparison Of Non-Uniform Optimal Quantizer Designs For Speech Coding With Adaptive Critics And Particle Swarm, Wenwei Zha, Ganesh K. Venayagamoorthy
Comparison Of Non-Uniform Optimal Quantizer Designs For Speech Coding With Adaptive Critics And Particle Swarm, Wenwei Zha, Ganesh K. Venayagamoorthy
Electrical and Computer Engineering Faculty Research & Creative Works
This paper presents the design of a companding non-uniform optimal scalar quantizer for speech coding. The quantizer is designed using two neural networks to perform the nonlinear transformation. These neural networks are used in the front and back ends of a uniform quantizer. Two approaches are presented in this paper namely adaptive critic designs (ACD) and particle swarm optimization (PSO), aiming to maximize the signal to noise ratio (SNR). The comparison of these optimal quantizer designs over bit rate range of 3 to 6 is presented. The perceptual quality of the coding is evaluated by the International Telecommunication Union''s Perceptual …
Aircraft Cabin Noise Minimization Via Neural Network Inverse Model, Xiao Hu, G. Clark, M. Travis, J. L. Vian, Donald C. Wunsch
Aircraft Cabin Noise Minimization Via Neural Network Inverse Model, Xiao Hu, G. Clark, M. Travis, J. L. Vian, Donald C. Wunsch
Electrical and Computer Engineering Faculty Research & Creative Works
This paper describes research to investigate an artificial neural network (ANN) approach to minimize aircraft cabin noise in flight. The ANN approach is shown to be able to accurately model the non-linear relationships between engine unbalance, airframe vibration, and cabin noise to overcome limitations associated with traditional linear influence coefficient methods. ANN system inverse models are developed using engine test-stand vibration data and on-airplane vibration and noise data supplemented with influence coefficient empirical data. The inverse models are able to determine balance solutions that satisfy cabin noise specifications. The accuracy of the ANN model with respect to the real system …
Forecasting Series-Based Stock Price Data Using Direct Reinforcement Learning, H. Li, Cihan H. Dagli, David Lee Enke
Forecasting Series-Based Stock Price Data Using Direct Reinforcement Learning, H. Li, Cihan H. Dagli, David Lee Enke
Engineering Management and Systems Engineering Faculty Research & Creative Works
A significant amount of work has been done in the area of price series forecasting using soft computing techniques, most of which are based upon supervised learning. Unfortunately, there has been evidence that such models suffer from fundamental drawbacks. Given that the short-term performance of the financial forecasting architecture can be immediately measured, it is possible to integrate reinforcement learning into such applications. In this paper, we present the novel hybrid view for a financial series and critic adaptation stock price forecasting architecture using direct reinforcement. A new utility function called policies-matching ratio is also proposed. The need for the …
Development And Analysis Of A Feedback Treatment Strategy For Parturient Paresis Of Cows, Radhakant Padhi, S. N. Balakrishnan
Development And Analysis Of A Feedback Treatment Strategy For Parturient Paresis Of Cows, Radhakant Padhi, S. N. Balakrishnan
Mechanical and Aerospace Engineering Faculty Research & Creative Works
An intelligent on-line feedback treatment strategy based on nonlinear optimal control theory is presented for the parturient paresis of cows. A limitation in the development of an existing nonlinear mathematical model for the homogeneous system is addressed and further modified to incorporate a control input. A neural network based optimal feedback controller is synthesized for the treatment of the disease. Detailed studies are used to analyze the effectiveness of a feedback medication strategy and it is compared with the current "impulse" strategy. The results show that while the current practice may fail in some cases, especially if it is carried …
Time Series Prediction With A Weighted Bidirectional Multi-Stream Extended Kalman Filter, Donald C. Wunsch, Xiao Hu
Time Series Prediction With A Weighted Bidirectional Multi-Stream Extended Kalman Filter, Donald C. Wunsch, Xiao Hu
Electrical and Computer Engineering Faculty Research & Creative Works
This paper describes the use of a multi-stream extended Kalman filter (EKF) to tackle the IJCNN 2004 challenge problem - time series prediction on CATS benchmark. A weighted bidirectional approach was adopted in the experiments to incorporate the forward and backward predictions of the time series. EKF is a practical, general approach to neural networks training. It consists of the following: 1) gradient calculation by backpropagation through time (BPTT); 2) weight updates based on the extended Kalman filter; and 3) data presentation using multi-stream mechanics.
Adaptive Load Frequency Control Of Nigerian Hydrothermal System Using Unsupervised And Supervised Learning Neural Networks, Ganesh K. Venayagamoorthy, U. O. Aliyu, S. Y. Musa
Adaptive Load Frequency Control Of Nigerian Hydrothermal System Using Unsupervised And Supervised Learning Neural Networks, Ganesh K. Venayagamoorthy, U. O. Aliyu, S. Y. Musa
Electrical and Computer Engineering Faculty Research & Creative Works
This work presents a novel load frequency control design approach for a two-area power system that relies on unsupervised and supervised learning neural network structure. Central to this approach is the prediction of the load disturbance of each area at every minute interval that is uniquely assigned to a cluster via unsupervised learning process. The controller feedback gains corresponding to each cluster center are determined using modal control technique. Thereafter, supervised learning neural network (SLNN) is employed to learn the mapping between each cluster center and its feedback gains. A real time load disturbance in either or both areas activates …
Vibration Analysis Via Neural Network Inverse Models To Determine Aircraft Engine Unbalance Condition, Xiao Hu, J. L. Vian, Donald C. Wunsch, J. R. Slepski
Vibration Analysis Via Neural Network Inverse Models To Determine Aircraft Engine Unbalance Condition, Xiao Hu, J. L. Vian, Donald C. Wunsch, J. R. Slepski
Electrical and Computer Engineering Faculty Research & Creative Works
This paper describes the use of artificial neural networks (ANNs) with the vibration data from real flight tests for detecting engine health condition - mass imbalance herein. Order-tracking data, calculated from time series is used as the input to the neural networks to determine the amount and location of mass imbalance on aircraft engines. Several neural network methods, including multilayer perceptron (MLP), extended Kalman filter (EKF) and support vector machines (SVMs) are used in the neural network inverse model for the performance comparison. The promising performances are presented at the end.
Neural Networks Skin Tumor Diagnostic System, Zhao Zhang, William V. Stoecker, Randy Hays Moss
Neural Networks Skin Tumor Diagnostic System, Zhao Zhang, William V. Stoecker, Randy Hays Moss
Electrical and Computer Engineering Faculty Research & Creative Works
In this study, a malignant melanoma diagnostic system is designed using a straightforward neural network with the back-propagation learning algorithm. Eleven features are automatically extracted from skin tumor images. The correct diagnostic rate of this system is better than the average rate of 16 dermatologists who based their diagnosis with only the slide images.
A Comparison Of Dual Heuristic Programming (Dhp) And Neural Network Based Stochastic Optimization Approach On Collective Robotic Search Problem, Nian Zhang, Donald C. Wunsch
A Comparison Of Dual Heuristic Programming (Dhp) And Neural Network Based Stochastic Optimization Approach On Collective Robotic Search Problem, Nian Zhang, Donald C. Wunsch
Electrical and Computer Engineering Faculty Research & Creative Works
An important application of mobile robots is searching a region to locate the origin of a specific phenomenon. A variety of optimization algorithms can be employed to locate the target source, which has the maximum intensity of the distribution of some detected function. We propose two neural network algorithms: stochastic optimization algorithm and dual heuristic programming (DHP) to solve the collective robotic search problem. Experiments were carried out to investigate the effect of noise and the number of robots on the task performance, as well as the expenses. The experimental results showed that the performance of the dual heuristic programming …
Probabilistic Neural Networks For Multi-Class Tissue Discrimination With Gene Expression Data, Rui Xu, Donald C. Wunsch
Probabilistic Neural Networks For Multi-Class Tissue Discrimination With Gene Expression Data, Rui Xu, Donald C. Wunsch
Electrical and Computer Engineering Faculty Research & Creative Works
With the emergence and rapid advancement of DNA microarray technologies, construction of gene expression profiles for different cancer types has already become a promising means for cancer diagnosis and treatment. Most previous research has focused on binary classification. Here, we use a probabilistic neural network (PNN) for multi-classification of cancer data. The experimental results demonstrate the effectiveness of the PNN in addressing gene expression data.
State-Constrained Agile Missile Control With Adaptive-Critic-Based Neural Networks, Dongchen Han, S. N. Balakrishnan
State-Constrained Agile Missile Control With Adaptive-Critic-Based Neural Networks, Dongchen Han, S. N. Balakrishnan
Mechanical and Aerospace Engineering Faculty Research & Creative Works
In this study, we develop an adaptive-critic-based controller to steer an agile missile that has a constraint on the minimum flight Mach number from various initial Mach numbers to a given final Mach number in minimum time while completely reversing its flightpath angle. This class of bounded state space, free final time problems is very difficult to solve due to discontinuities in costates at the constraint boundaries. We use a two-neural-network structure called "adaptive critic" in this study to carry out the optimization process. This structure obtains an optimal controller through solving optimal control-related equations resulting from a Hamiltonian formulation. …
Comparison Of Heuristic Dynamic Programming And Dual Heuristic Programming Adaptive Critics For Neurocontrol Of A Turbogenerator, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Ronald G. Harley
Comparison Of Heuristic Dynamic Programming And Dual Heuristic Programming Adaptive Critics For Neurocontrol Of A Turbogenerator, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Ronald G. Harley
Electrical and Computer Engineering Faculty Research & Creative Works
This paper presents the design of an optimal neurocontroller that replaces the conventional automatic voltage regulator (AVR) and the turbine governor for a turbogenerator connected to the power grid. The neurocontroller design uses a novel technique based on the adaptive critic designs (ACDs), specifically on heuristic dynamic programming (HDP) and dual heuristic programming (DHP). Results show that both neurocontrollers are robust, but that DHP outperforms HDP or conventional controllers, especially when the system conditions and configuration change. This paper also shows how to design optimal neurocontrollers for nonlinear systems, such as turbogenerators, without having to do continually online training of …
Proper Orthogonal Decomposition Based Feedback Optimal Control Synthesis Of Distributed Parameter Systems Using Neural Networks, Radhakant Padhi, S. N. Balakrishnan
Proper Orthogonal Decomposition Based Feedback Optimal Control Synthesis Of Distributed Parameter Systems Using Neural Networks, Radhakant Padhi, S. N. Balakrishnan
Mechanical and Aerospace Engineering Faculty Research & Creative Works
A new method for optimal control design of distributed parameter systems is presented in this paper. The concept of proper orthogonal decomposition is used for the model reduction of distributed parameter systems to form a reduced order lumped parameter problem. The optimal control problem is then solved in the time domain, in a state feedback sense, following the philosophy of ''adaptive critic'' neural networks. The control solution is then mapped back to the spatial domain using the same basis functions. Numerical simulation results are presented for a linear and nonlinear one-dimensional heat equation problem in an infinite time regulator framework.
Abnormal Cell Detection Using The Choquet Integral, R. Joe Stanley, James M. Keller, Charles William Caldwell, Paul D. Gader
Abnormal Cell Detection Using The Choquet Integral, R. Joe Stanley, James M. Keller, Charles William Caldwell, Paul D. Gader
Electrical and Computer Engineering Faculty Research & Creative Works
Automated Giemsa-banded chromosome image research has been largely restricted to classification schemes associated with isolated chromosomes within metaphase spreads. In normal human metaphase spreads, there are 46 chromosomes occurring in homologous pairs for the autosomal classes 1-22 and the X chromosome for females. Many genetic abnormalities are directly linked to structural and/or numerical aberrations of chromosomes within metaphase spreads. Cells with the Philadelphia chromosome contain an abnormal chromosome for class 9 and for class 22, leaving a single normal chromosome for each class. A data-driven homologue matching technique is applied to recognizing normal chromosomes from classes 9 and 22. Homologue …
Adaptive Critic Based Neuro-Observer, Xin Liu, S. N. Balakrishnan
Adaptive Critic Based Neuro-Observer, Xin Liu, S. N. Balakrishnan
Mechanical and Aerospace Engineering Faculty Research & Creative Works
A new Neural Network (NN) based observer design method for nonlinear systems represented by nonlinear dynamics and linear/nonlinear measurement is proposed in this paper. In this new approach, as the first step, the observer design problem is changed into a "controller" design problem by establishing the error dynamics, and then the Adaptive Critic (AC) based approach is applied on this error dynamics to design a 'controller', such that the errors are driven to zero. The resulting observer has inherent robustness from the AC based design approach. Some simulations are presented to illustrate the effectiveness of this approach.