Fuzzy perceptron neural networks for classifiers with numerical data and linguistic rules as inputs

This paper presents a novel learning algorithm of fuzzy perceptron neural networks (FPNNs) for classifiers that utilize expert knowledge represented by fuzzy IF-THEN rules as well as numerical data as inputs. The conventional linear perceptron network is extended to a second-order one, which is much more flexible for defining a discriminant function. In order to handle fuzzy numbers in neural networks, level sets of fuzzy input vectors are incorporated into perceptron neural learning. At different levels of the input fuzzy numbers, updating the weight vector depends on the minimum of the output of the fuzzy perceptron neural network and the corresponding nonfuzzy target output that indicates the correct class of the fuzzy input vector. This minimum is computed efficiently by employing the modified vertex method to lessen the computational load and the training time required. Moreover, the pocket algorithm, called fuzzy pocket algorithm, is introduced into our fuzzy perceptron learning scheme to solve the nonseparable problems. Simulation results demonstrate the effectiveness of the proposed FPNN model.