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Full-Text Articles in Engineering
Development Of Hiv Model And Its Simulation, D. K. Chaturvedi, Pritam Singh, S. K. Gaur, D. S. Mishra
Development Of Hiv Model And Its Simulation, D. K. Chaturvedi, Pritam Singh, S. K. Gaur, D. S. Mishra
D. K. Chaturvedi Dr.
The article discusses model developmetn for HIV infected population using the System Dynamics technique. The technique has an advantage over conventional modeling technique as it is dependent more on causal relationships that involve qualitative and quantitative variables to model socioeconomic problems of a complex nature. The model has been simulated and the results haven been compared witht he available data.
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 …
Fuzzified Neural Network Approach For Load Forecasting Problems, D. K. Chaturvedi, P. S. Satsangi, P. K. Kalra
Fuzzified Neural Network Approach For Load Forecasting Problems, D. K. Chaturvedi, P. S. Satsangi, P. K. Kalra
D. K. Chaturvedi Dr.
In load forecasting, the operator or the concerned person uses his or her experience and intuitions to obtain a good guess of the load demand. This guess is normally supported by sophisticated mathematical prediction techniques. The short term load not only varies from hour to hour, but is also influenced by the nature of events, load demand, the type of the load considered, seasonal variations, weekend day or holidays, and also by sudden demand and loss of load. Accordingly, it is quite clear that the electrical load-forecasting problem is quite difficult to model with mathematical difference or differential equations. In …