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In the normal fuzzy logic control (FLC) system, both the membership functions and the rule sets are usually decided upon subjectively, case by case. The application of Genetic Algorithm(GA) could lead to proper selection of membership functions and rule base objectively. In this paper, the optimisation of membership functions of a FLC for a fed-batch fermentor is carried out with help of Genetic Algorithm (GA). Results are found to be satisfactory.

The estimation of parameters of process dynamics is an important phase in the modeling of systems and in the design of many instrumentation systems. This problem has been taken up by many researchers and many methods and algorithms have been formulated. To the best of the author's knowledge, a compact and simple recursive method is not available in the literature for a fixed memory type estimator from the measurements of input and output data sets. In this proposed paper a simple and straightforward on-line method for parameter estimation of multivariable discrete-data systems is presented. The parameters are determined taking into consideration the present and n + m − 1 past measurements for a nth-order process with m inputs. To reduce the computational burden, a recursive scheme is detailed for updating the estimate of the parameters as soon as the new data arrives. This recursive scheme can be started with arbitrary parameter values. But to have an easy starting procedure, a simple starting matrix with a recursive computational procedure is also given. This method is suitable for situations involving tracking of slowly time-varying parameters that remain unchanged at least for n + m iterations.

Methods for recursive computation of the pseudo-inverse of a sequence of horizontal rectangular matrices in which the successive matrices differ by: (i) one column only, and (ii) one row only, are described. In both the above cases, the pseudo-inverse of the first matrix of the sequence is assumed to be known. The proposed methods avoid the direct inversion of matrices. These new methods can also be used to compute the pseudo-inverse of any arbitrary matrix iteratively. The two distinct cases mentioned above are considered in two parts in this paper. These situations arise in the fixed memory adaptive identification of discrete-data systems. ? 1982.