V.15:14 (654-657): NeuroGenetic Optimizer, version 2.5 by Howard Bandy, Ph.D.
Product Description
NeuroGenetic Optimizer, Version 2.5 (Biocomp Systems, Inc.). Neural network development system. by Howard Bandy, Ph.D.
Could you use some additional brainpower to attack difficult modeling and prediction problems? Neuro-Genetic Optimizer (NGO), neural network development system from BioComp Systems, offers several unique neural network architectures, including some designed to help with time series analysis and prediction.
A neural network is an artificial intelligence program capable of learning the relationships present among the data through a training process. A genetic algorithm mimics the processes associated with evolution. In NGO, genetic algorithms help find efficient and effective neural networks.
MODEL DEVELOPMENT
In any modeling and simulation process, the goal is to construct a mathematical model of a real relationship with the hope of better understanding the relationship. We want to explain a result in terms of the factors that affect it, perhaps to predict future behavior. Models are often divided into two broad types: classification and estimation (or function approximation). They are further divided according to whether the time sequence of the data is important.
Could you use some additional brainpower to attack difficult modeling and prediction problems? Neuro-Genetic Optimizer (NGO), neural network development system from BioComp Systems, offers several unique neural network architectures, including some designed to help with time series analysis and prediction.
A neural network is an artificial intelligence program capable of learning the relationships present among the data through a training process. A genetic algorithm mimics the processes associated with evolution. In NGO, genetic algorithms help find efficient and effective neural networks.
MODEL DEVELOPMENT
In any modeling and simulation process, the goal is to construct a mathematical model of a real relationship with the hope of better understanding the relationship. We want to explain a result in terms of the factors that affect it, perhaps to predict future behavior. Models are often divided into two broad types: classification and estimation (or function approximation). They are further divided according to whether the time sequence of the data is important.
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