Synthesis of Fault-Tolerant Feedforward Neural Networks Using Minimax Optimization  

Oleh:

Vidyasagar, M. ; Deodhare, D. ; Keerthi, S. Sathiya

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 9 no. 5 (1998), page 891-900.
Topik: NEURAL NETWORKS; synthesis; fault - tolerant; neural networks; minimax optimization

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.3 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

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 very nearly equals that of the original minimax problem. Networks synthesized using these methods, though not always fault tolerant, exhibit an acceptable degree of partial fault tolerance.

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