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Full-Text Articles in Engineering
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 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.
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 …
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 …
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.
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 …
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 …
A Committee Of Neural Networks For Automatic Speaker Recognition (Asr) Systems, Viresh Moonasar, Ganesh K. Venayagamoorthy
A Committee Of Neural Networks For Automatic Speaker Recognition (Asr) Systems, Viresh Moonasar, Ganesh K. Venayagamoorthy
Electrical and Computer Engineering Faculty Research & Creative Works
This paper describes how the results of speaker verification systems can be improved and made robust with the use of a committee of neural networks for pattern recognition rather than the conventional single-network decision system. It illustrates the use of a supervised learning vector quantization neural network as the pattern classifier. Linear predictive coding and cepstral signal processing techniques are utilized to form hybrid feature parameter vectors to combat the effect of decreased recognition success with increased group size (number of speakers to be recognized)
A Parallel Computer-Go Player, Using Hdp Method, Donald C. Wunsch, Xindi Cai
A Parallel Computer-Go Player, Using Hdp Method, Donald C. Wunsch, Xindi Cai
Electrical and Computer Engineering Faculty Research & Creative Works
The game of Go has simple rules to learn but requires complex strategies to play well, and, the conventional tree search algorithm for computer games is not suited for Go program. Thus, the game of Go is an ideal problem domain for machine learning algorithms. This paper examines the performance of a 19x19 computer Go player, using heuristic dynamic programming (HDP) and parallel alpha-beta search. The neural network based Go player learns good Go evaluation functions and wins about 30% of the games in a test series on 19x19 board
Comparison Of A Heuristic Dynamic Programming And A Dual Heuristic Programming Based Adaptive Critics Neurocontroller For A Turbogenerator, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Ronald G. Harley
Comparison Of A Heuristic Dynamic Programming And A Dual Heuristic Programming Based Adaptive Critics Neurocontroller For 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 a neurocontroller for a turbogenerator that augments/replaces the conventional automatic voltage regulator and the turbine governor. The neurocontroller uses a novel technique based on the adaptive critic designs with emphasis on heuristic dynamic programming (HDP) and dual heuristic programming (DHP). Results are presented to show that the DHP based neurocontroller is robust and performs better than the HDP based neurocontroller, as well as the conventional controller, especially when the system conditions and configuration changes.
Efficient Training Techniques For Classification With Vast Input Space, Donald C. Wunsch, Emad W. Saad, J. J. Choi, J. L. Vian
Efficient Training Techniques For Classification With Vast Input Space, Donald C. Wunsch, Emad W. Saad, J. J. Choi, J. L. Vian
Electrical and Computer Engineering Faculty Research & Creative Works
Strategies to efficiently train a neural network for an aerospace problem with a large multidimensional input space are developed and demonstrated. The neural network provides classification for over 100,000,000 data points. A query-based strategy is used that initiates training using a small input set, and then augments the set in multiple stages to include important data around the network decision boundary. Neural network inversion and oracle query are used to generate the additional data, jitter is added to the query data to improve the results, and an extended Kalman filter algorithm is used for training. A causality index is discussed …
Data-Driven Homologue Matching For Chromosome Identification, R. Joe Stanley, James M. Keller, Paul D. Gader, Charles William Caldwell
Data-Driven Homologue Matching For Chromosome Identification, R. Joe Stanley, James M. Keller, Paul D. Gader, Charles William Caldwell
Electrical and Computer Engineering Faculty Research & Creative Works
Karyotyping involves the visualization and classification of chromosomes into standard classes. In "normal" human metaphase spreads, chromosomes occur in homologous pairs for the autosomal classes 1-22, and X chromosome for females. Many existing approaches for performing automated human chromosome image analysis presuppose cell normalcy, containing 46 chromosomes within a metaphase spread with two chromosomes per class. This is an acceptable assumption for routine automated chromosome image analysis. However, many genetic abnormalities are directly linked to structural or numerical aberrations of chromosomes within the metaphase spread. Thus, two chromosomes per class cannot be assumed for anomaly analysis. This paper presents the …
Voice Recognition Using Neural Networks, Ganesh K. Venayagamoorthy, Viresh Moonasar, K. Sandrasegaran
Voice Recognition Using Neural Networks, Ganesh K. Venayagamoorthy, Viresh Moonasar, K. Sandrasegaran
Electrical and Computer Engineering Faculty Research & Creative Works
One solution to the crime and illegal immigration problem in South Africa may be the use of biometric techniques and technology. Biometrics are methods for recognizing a user based on unique physiological and/or behavioural characteristics of the user. This paper presents the results of ongoing work into using neural networks for voice recognition