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Neurofuzzy adaptive modelling and construction of nonlinear dynamical processes

Neurofuzzy adaptive modelling and construction of nonlinear dynamical processes

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This chapter addresses a range of neurofuzzy algorithms that automatically construct parsimonious models of nonlinear dynamical processes. The process dynamics are typically unknown and complex (i.e. multivariate, non linear and time varying) making the generation of accurate models by conventional methods. In these instances more sophisticated (intelligent) modelling techniques are required. Weight identification, known as learning, is achieved by optimising the weights with respect to some error criteria across a set of input-output pairs. This set is known as a teaching set and must adequately represent the systems dynamic behaviour. Typically this type of modelling is termed black box modelling where the internal representation does not reflect the behaviour of the physical system.

Inspec keywords: fuzzy set theory; nonlinear dynamical systems; modelling; adaptive systems; multivariable systems; large-scale systems; fuzzy systems; identification; neurocontrollers; time-varying systems

Other keywords: error criteria; multivariate system; weight identification; nonlinear dynamical process; neurofuzzy adaptive modelling; intelligent modelling techniques; system identification; time varying system; black box modelling; teaching set; system modelling

Subjects: Nonlinear control systems

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