A method based on genetic algorithm (GA), a simple clustering procedure for rule base generation, and weighted least squares estimation is proposed to construct a Takagi-Sugeno-Kang (TSK) fuzzy inference system directly from numerical data. The rule-base generation method takes the approach of independently clustering input and output spaces, respectively, and assigning a weight to each rule to capture the relation in input-output data. Genetic process learns the number of linguistic terms per variable and the certainty factors of the rules (indirectly the membership-function parameters of the premise part of the fuzzy rules), and the weighted least squares method is used to determine the consequent part of the fuzzy rules. Simulation results on forecasting the stock market and a benchmark case study are included.

A generalized pipeline cellular array has been proposed which can perform all the basic operations such as multiplication, division, squaring, and square rooting. The different modes of operation are controlled by a single control line. An expression for time delay has been obtained. Further, it has been shown that these arithmetic operations can be overlapped in the pipe in any desired sequence, and thus significant speed improvement can be achieved. The array is fully iterative and hence is suitable for large-scale integration (LSI).