Adaptive neurofuzzy systems for difficult modelling and control problems

The ability to learn is the cornerstone of current neural technology. It was responsible for their decline in the late sixties when it was shown that the perceptron training algorithm could not be easily extended to multilayered networks, and also the revival of interest in these techniques was initiated by the discovery of a multilayer network adaptation rule. Artificial neural networks attempt to mimic a human´s information processing capabilities by building neuronally inspired systems which learn to interact with their environment in a desirable fashion. There are many engineering problems which could benefit from the use such of such systems, although there are also problems with applying these adaptive networks. Most artificial neural networks can be regarded as “black box” learning systems; they are difficult initialise as knowledge is stored in an opaque fashion and validation can only performed using input/output data; their internal structure provides little information to an engineer. In addition, the generalisation, modelling and learning abilities of these networks are generally poorly understood, although such results are necessary when these adaptive systems are applied online in safety critical situations