Synthesis of fault-tolerant feedforward neural networks using minimax optimization

—In this paper we examine a technique by which fault tolerance can be embedded into a feedforward network leading to a network tolerant to the loss of a node and its associated weights. The fault tolerance problem for a feedforward network is formulated as a constrained minimax optimization problem. Two different methods are used to solve it. In the first method, the constrained minimax optimization problem is converted to a sequence of unconstrained least-squares optimization problems, whose solutions converge to the solution of the original minimax problem. An efficient gradient-based minimization technique, specially tailored for nonlinear least-squares optimization, is then applied to perform the unconstrained minimization at each step of the sequence. Several modifications are made to the basic algorithm to improve its speed of convergence. In the second method a different approach is used to convert the problem to a single unconstrained minimization problem whose solution ve...

• Type: Journal
• Year: 1998
• Where: TNN
• Authors: Dipti Deodhare, M. Vidyasagar, S. Sathiya Keerthi
• Keywords: Constrained Minimax Optimization, Fault Tolerance, Minimax Optimization Problem, TNN 1998