Presents a novel synthesis procedure to realize an associative memory using the Generalized-Brain-State-in-a-Box (GBSB) neural model. The implementation yields an interconnection structure thatâ€¦ (More)

We propose learning and forgetting techniques for the generalized brain-state-in-a-box (BSB) based associative memories. A generalization of the BSB model allows each neuron to have its own bias andâ€¦ (More)

We use a penalty function approach and the gradient method to solve minimum norm problems. A class of penalty functions is introduced that allows one to transform constrained optimization minimumâ€¦ (More)

This paper is concerned with utilizng analog circuits to solve various linear and nonlinear programming problems. The dynamics of these circuits are analyzed. A new nonlinear programming network andâ€¦ (More)

This paper is concerned with utilizing neural networks and analog circuits to solve constrained optimization problems. A novel neural network architecture is proposed for solving a class of nonlinearâ€¦ (More)

Deals with the use of neural networks to solve linear and nonlinear programming problems. The dynamics of these networks are analyzed. In particular, the dynamics of the canonical nonlinearâ€¦ (More)