Bøger af Uma Maheshwari

Soft computing deals with imprecision, uncertainty, partial truth, and approximation to achieve tractability, robustness and low solution cost. Soft computing covers a wide range of application areas, including optimisation, data analysis and data mining, computer graphics and vision, prediction and diagnosis, design, intelligent control, and traffic and transportation systems. Bioinformatics is the application of computer technology to the management and manipulation of biological information. Soft computing offers a promising approach to achieve efficient and reliable heuristic solution for the bioinformatics problem areas such as clustering, pattern recognition and prediction related domains. In this book, soft computing methodologies for cluster analysis of microarray data using Kohonen''s Self Organising Maps and prediction of genes using Back Propagation Network and Learning Vector Quantization Network are discussed.

Neural networks are processing devices which are either algorithms or actual hard wares. Their designs are motivated by the design and functioning of human brains and components thereof. Neural networks provide improved performance over conventional technologies in the areas of machine vision, robust pattern detection, signal filtering, virtual reality, data segmentation, data compression, data mining, text mining, artificial life, adaptive control, optimization and scheduling, complex mapping and many more. In this book the fundamental simulation methodologies of the neural networks - McCulloch Pitts neuron model, Hebb''s network, perceptron network, ADALINE neuron model, MADALINE neurons model, hetero associative memory network, auto associative memory network, bidirectional associative memory network, discrete Hopfield network, back propagation network, self organizing map network, learning vector quantization network, max net, mexican hat network, hamming net and counter propagation network are described and illustrated with the help of algorithms, MATLAB source codes and outputs.