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A GRG2-Based System for Training Neural Networks: Design and Computational Experience
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1993
Year
Artificial IntelligenceEvolving Neural NetworkEngineeringMachine LearningData ScienceArtificial Neural NetworksPattern RecognitionMachine Learning ModelIntelligent OptimizationGrg2-based SystemComputer EngineeringLarge Scale OptimizationComputational ExperienceComputer ScienceNeural NetworksBrain-like ComputingNeural Architecture SearchRecurrent Neural Network
Artificial neural networks is a very active area of research in artificial intelligence. They are better than existing methods for many problems in pattern recognition and pattern matching. They are trained through examples rather than being programmed. Training neural networks is a nonlinear minimization problem. The currently popular algorithm for training neural networks is called back-propagation, a form of steepest descent technique. This paper presents a new training system based on GRG2, a widely distributed nonlinear optimization software. Comparisons with back-propagation based upon three benchmark problems suggest not only that the GRG2-based system is much faster, more robust and offers solutions with better quality, but also offers better scalability to larger problems. This paper is thus a clear example of the kinds of contributions that optimization theory can make to artificial intelligence. INFORMS Journal on Computing, ISSN 1091-9856, was published as ORSA Journal on Computing from 1989 to 1995 under ISSN 0899-1499.