Fuzzy, Neural and Expert Systems

Provides a working knowledge of the principles of these techniques and an awareness of the evolving technology. Topics covered are - fuzzy systems; fuzzy vs. crisp signals, fuzzification, membership functions, design of rule base, Zadeh's rules of inference, cross membership, defuzzification, mean of maxima and centroid, comparison of fuzzy & PID controllers, non- linearity & time delays: self-adaptive fuzzy controllers; initiation & learning, genetic algorithms: artificial neural networks (ANN); the neuron, multi-layer perceptrons, activation functions, training methods, back propagation, steepest descent, evaluation of Jacobians, data encoding & pre-processing: radial basis function networks; centres, spreads and weights: use of ANNs for inferential estimation, dynamic modelling & optimisation: novel architectures & developments: knowledge based systems, history & examples, data & knowledge bases, inference engine, encoding facts, attributes & properties, inheritance, descriptive rules, identification of the expert, knowledge elicitation, rule based inferencing, user interface, real-time issues, project management.

Demonstrations and case studies of industrial applications are provided which are based on G2 and MATLAB toolboxes. Practicals use fuzzy logic and ANN toolboxes to give hands-on experience of estimation and control techniques. [details]