This work reviews the recent history of the design of neurocomputing algorithms and discusses the shortcomings that motivated the design of newer algorithms. In particular, it is proposed that recent results in a vari...
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This work reviews the recent history of the design of neurocomputing algorithms and discusses the shortcomings that motivated the design of newer algorithms. In particular, it is proposed that recent results in a variety of scientific fields can be brought to bear to address a number of limitations in the current second generation of non-linear fixed structure networks. A new method called Self Organizing Neural Network (SONN) algorithm is reviewed, and its performance compared with the Backpropagation algorithm (Generalized Delta Rule). Previously presented results of time series prediction and signal separation are reviewed here. The SONN is an algorithm that constructs its own network topology during training, which is shown to be much smaller than the BP network, faster to train, and free from the trial-and-error nesign that characterizes BP.
An algorithm called the self-organizing neural network (SONN) is described, and its use as a supervised learning architecture is demonstrated. The algorithm constructs a network, chooses the neuron functions, and adju...
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An algorithm called the self-organizing neural network (SONN) is described, and its use as a supervised learning architecture is demonstrated. The algorithm constructs a network, chooses the neuron functions, and adjusts the weights. The final network structure is optimal in the sense that it uses simulated annealing in the model search. The results (number of weights, complexity of the final structure, computer time, and model accuracy) are compared to the back-propagation algorithm. They show that SONN constructs a simpler, more accurate model, requiring fewer training data and epochs.< >
This work introduces a new method called Self Organizing Neural Network (SONN) algorithm and compares its performance with Back Propagation in a signal separation application. The problem is to separate two signals; a...
This work introduces a new method called Self Organizing Neural Network (SONN) algorithm and compares its performance with Back Propagation in a signal separation application. The problem is to separate two signals; a modem data signal and a male speech signal, added and transmitted through a 4 khz channel. The signals are sampled at 8 khz, and using supervised learning, an attempt is made to reconstruct them. The SONN is an algorithm that constructs its own network topology during training, which is shown to be much smaller than the BP network, faster to trained, and free from the trial-and-error network design that characterize BP.
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