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Full-Text Articles in Engineering
Dayalbagh Educational Institute Soft Computing Edge Cutting Technology Lab (Deisel), D. K. Chaturvedi
Dayalbagh Educational Institute Soft Computing Edge Cutting Technology Lab (Deisel), D. K. Chaturvedi
D. K. Chaturvedi Dr.
Dayalbagh Educational Institute Soft Computing Edge Cutting Technology Lab (DEISEL) Group consiting of a Professor Incharge, four Teaching Staff members, five Non-Teaching Staff members, five Ph.D. Students, six M. Tech. Students. The objective of DEISEL is to to exploit the tolerance for imprecision uncertainty, approximate reasoning and partial truth to achieve tractability, robustness, low solution cost, and close resemblance with human like decision making to find an approximate solution to an imprecisely/precisely formulated problem. The challenge is to exploit the tolerance for imprecision by devising methods of computation which lead to an acceptable solution at low cost. This, in essence, …
Programs Of Fuzy Systems, D. K. Chaturvedi
Programs Of Fuzy Systems, D. K. Chaturvedi
D. K. Chaturvedi Dr.
The zip file contains c programs of fuzzy system.
Matlab Program Of Genetic Algorithms, D. K. Chaturvedi
Matlab Program Of Genetic Algorithms, D. K. Chaturvedi
D. K. Chaturvedi Dr.
The zip file contains Matlab program of genetic algorithms and their varients.
Ann /Gn Programs, D. K. Chaturvedi
Ann /Gn Programs, D. K. Chaturvedi
D. K. Chaturvedi Dr.
The file contains programms of multi layer feedforward backpropagation ANN, GN and their varients.
A Generalized Neuron Based Adaptive Power System Stabilizer, D. K. Chaturvedi, O. P. Malik, P. K. Kalra
A Generalized Neuron Based Adaptive Power System Stabilizer, D. K. Chaturvedi, O. P. Malik, P. K. Kalra
D. K. Chaturvedi Dr.
Artificial neural networks (ANNs) can be used as intelligent controllers to control nonlinear, dynamic systems through learning, which can easily accommodate the nonlinearities and time dependencies. However, they require long training time and large numbers of neurons to deal with complexproblems. To overcome these drawbacks, a generalised neuron (GN) has been developed that requires much smaller training data and shorter training time. Taking benefit of these characteristics of the GN, a new generalised neuron-based adaptive power system stabiliser (GNPSS) is proposed. The GNPSS consists of a GN as an identifier, which tracks the dynamics of the plant, and a GN …
Neuro-Fuzzy Approach For Development Of New Neuron Model, Manmohan, D. K. Chaturvedi, P. S. Satsangi, P. K. Kalra
Neuro-Fuzzy Approach For Development Of New Neuron Model, Manmohan, D. K. Chaturvedi, P. S. Satsangi, P. K. Kalra
D. K. Chaturvedi Dr.
The training time of ANN depends on size of ANN (i.e. number of hidden layers and number of neurons in each layer), size of training data, their normalization range and type of mapping of training patterns (like X–Y, X–DY, DX–Y and DX–DY), error functions and learning algorithms. The efforts have been done in past to reduce training time of ANN by selection of an optimal network and modification in learning algorithms. In this paper, an attempt has been made to develop a new neuron model using neuro-fuzzy approach to overcome the problems of ANN incorporating the features of fuzzy systems …
Artificial Neural Network Learning Using Improved Genetic Algorithms, D. K. Chaturvedi
Artificial Neural Network Learning Using Improved Genetic Algorithms, D. K. Chaturvedi
D. K. Chaturvedi Dr.
The feedforward back-propagation artificial neural networks (ANN) are widely used to control the various industrial process, for modelling, simulation of systems and forecasting. The backpropagation learning has various drawbacks such as slowness in learning, stuck in local minima, requies functional derivative of aggregation function and thresholding function to minimize error function. Various researchers have suggested a number of improvement in simple back-propagation learning algorithm developed by Widrow and Holf in 1956. In this paper, a program is developed for feedforward artificial neural network with genetic algorithm (GA) as the learning mechanism to overcome some of the disadvantages of back-propagation learning …
Load Frequency Control: A Generalized Neural Network Approach, D. K. Chaturvedi, P. S. Satsangi, P. K. Kalra
Load Frequency Control: A Generalized Neural Network Approach, D. K. Chaturvedi, P. S. Satsangi, P. K. Kalra
D. K. Chaturvedi Dr.
Variation in load frequency is an index for normal operation of power systems. When load perturbation takes place anywhere in any area of the system, it will affect the frequency at other areas also. To control load frequency of power systems various controllers are used in different areas. but due to non-linearities in the system components and alternators, these controllers cannot control the frequency quickly and efficiently. Simple neural networks which are in common use at present have various drawbacks like large training time, requirement of large number of neurons, etc. The present work deals with the developmetn of a …
Possible Applications Of Neural Nets To Power System Operation And Control, P. K. Kalra, Alok Srivastava, D. K. Chaturvedi
Possible Applications Of Neural Nets To Power System Operation And Control, P. K. Kalra, Alok Srivastava, D. K. Chaturvedi
D. K. Chaturvedi Dr.
Problems related to power system operation and control are complex and time consuming because of the non-linearities involved in their formulation and solution. Fast solutions to these problems can be obtained only through parallel processing. Neural nets provide massive parallel processing facilities and may also be used efficiently to model systems with non-linearities. The capabilities of neural nets can, therefore, be well utilized in modelling and processing problems related to power systems. In order to reduce the burden on computers, algorithms involving optimization and complex equations can be converted to heuristics. These heuristics can then be represented in terms of …