By Jeffrey W. Tweedale, Ivan Jordanov (auth.), Ivan Jordanov, Lakhmi C Jain (eds.)
This publication goals to advertise a pattern of present theoretical and alertness orientated clever structures examine particularly within the box of neural networks computing. It offers examples of experimental and real-world investigations that exhibit modern achievements and advances within the zone of clever structures.
This publication will turn out as a worthy resource of updated theoretical and alertness orientated study in clever platforms for researchers and postgraduate students.
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Srivastava Table 1. 15 Table 2. 2 Table 3. 2 training the neural network while remaining 20 (2x10) unseen patterns were used to test its performance. Utility derived load compositions may also be employed to train the fuzzy-neural network instead of theoretically generated data. 9. Pattern wise normalization of FCMI ensures accurate ranking under peak as well as off-peak times of the day, because the generators are ranked for the current load based on their relative severity. Table 5 data was used to fuzzify normalized FCMI values into five fuzzy classes.
The conventional multi-layer 28 K. Pal, M. Pandit, and L. Srivastava perceptron (MLP) networks are usually trained using gradient descent based on backpropagation (BP) algorithm, which is too slow for practical problems. Recently, several high performance algorithms have been developed to train MLP models that converge 10 to 100 times faster than the BP algorithm. These algorithms are based on numerical optimization techniques like conjugate gradient, quasi-Newton and Levenberg–Marquardt algorithms.
Pal, M. Pandit, and L. Srivastava perceptron (MLP) networks are usually trained using gradient descent based on backpropagation (BP) algorithm, which is too slow for practical problems. Recently, several high performance algorithms have been developed to train MLP models that converge 10 to 100 times faster than the BP algorithm. These algorithms are based on numerical optimization techniques like conjugate gradient, quasi-Newton and Levenberg–Marquardt algorithms. Out of these, Levenberg–Marquardt (LM) algorithm is found to be the fastest method for training moderate size feed forward neural networks [30].