A Neural Network with Interval Weights and Its Application to Fuzzy Regression Analysis
Hisao Ishibuchi, Hidehiko Okada, Hideo Tanaka · Transactions of the Society of Instrument and Control Engineers · 1992
This paper proposes a new architecture of neural networks and derives its learning algorithms.The neural network with the proposed architecture has interval weights and interval biases.A cost function is defined using the interval output from the neural network and the corresponding target interval.A learning algorithm is derived from the cost function in a similar manner as the back-propagation algorithm.Two variations of the learning algorithm are also derived based on the inclusion relation between the interval output and the target interval.A method of fuzzification is shown to apply the neural network to the fuzzy regression analysis.