An algorithmic approach for efficient identification of a fuzzy relational rule network (FR/sup 2/N) from data is presented. FR/sup 2/N uses a relational input partition for human-understandable modeling of linear interactions between the input variables. Mutual subsethood has been used to estimate the optimal interaction structure. An analytical relationship between the mutual subsethood measure and one of the parameters of the membership functions is derived. The use of this relationship results in a dramatic speed-up of the identification process.

A novel variation of the cross-coupled operational transconductance cell is presented in this paper. The conventional cross-coupled cell has a differential input linearity range that is dependent on the control voltage. The proposed design removes that restriction while allowing the same tunability using the control voltage. Its input may also be single or fully differential unlike the conventional cross-coupled cell, which requires a fully differential input. Results were confirmed using HSPICE simulations.

A unique and accurate approach for modeling semiconductor device characteristics and estimating IC parametric yield is described. Multilayer perceptron neural networks (MLPNN) are trained using error back propagation to model DC device characteristics measured at the final fabrication stage. Measurements of material and/or device characteristics taken at earlier fabrication stages are used to develop neural network models of the final DC parameters. A very good agreement has been found between the actual measurements and the MLPNN modeled parameters, and the resulting yield estimations are in excellent agreement with the actual yield.

Current-mode network transformations are presented from which it is possible to generate new current-mode circuits by performing simple operations on the network. These transformations are developed by interchanging the ports of a current-mode circuit and exploiting the properties of current-conveyors and RC networks. The new circuits need at most one additional current-conveyor for buffering purposes.>

A new approach to the problem of n-dimensional continuous and sampled-data function approximation using a two-layer neural network is presented. The generalized Nyquist theorem is introduced to solve for the optimum number of training examples in n-dimensional input space. Choosing the smallest but still sufficient set of training vectors results in a reduced learning time for the network. Analytical formulas and algorithm for training set size reduction are developed and illustrated by two-dimensional data examples.>