Add to ORKGA new methodology for neural learning is presented, whereby only a single iteration is required to train a feed-forward network with near-optimal results. To this aim, a virtual input layer is added to the multi-layer architecture. The virtual input layer is connected to the nominal input layer by a specird nonlinear transfer function, and to the fwst hidden layer by regular (linear) synapses. A sequence of alternating direction singular vrdue decompositions is then used to determine precisely the inter-layer synaptic weights. This algorithm exploits the known separability of the linear (inter-layer propagation) and nonlinear (neuron activation) aspects of information &ansfer within a neural network
In this paper we define on-line algorithms for neural-network training, based on the construction of...
Since the discovery of the back-propagation method, many modified and new algorithms have been propo...
An ongoing challenge in neural information processing is the following question: how do neurons adju...
The training of multilayer perceptron is generally a difficult task. Excessive training times and la...
In this paper a general class of fast learning algorithms for feedforward neural networks is introdu...
This study highlights on the subject of weight initialization in multi-layer feed-forward networks....
We present a general model for differentiable feed-forward neural networks. Its general mathematical...
Abstract—We develop, in this brief, a new constructive learning algorithm for feedforward neural net...
Network training algorithms have heavily concentrated on the learning of connection weights. Little ...
A positive reinforcement type learning algorithm is formulated for a stochastic feed-forward multila...
Neural Network models have received increased attention in the recent years. Aimed at achieving huma...
Reinforcement learning algorithms comprise a class of learning algorithms for neural networks. Reinf...
In this paper a review of fast-learning algorithms for multilayer neural networks is presented. From...
This paper demonstrates how a multi-layer feed-forward network may be trained, using the method of g...
A multilayer neural network based on multi-valued neurons is considered in the paper. A multivalued ...
In this paper we define on-line algorithms for neural-network training, based on the construction of...
Since the discovery of the back-propagation method, many modified and new algorithms have been propo...
An ongoing challenge in neural information processing is the following question: how do neurons adju...
The training of multilayer perceptron is generally a difficult task. Excessive training times and la...
In this paper a general class of fast learning algorithms for feedforward neural networks is introdu...
This study highlights on the subject of weight initialization in multi-layer feed-forward networks....
We present a general model for differentiable feed-forward neural networks. Its general mathematical...
Abstract—We develop, in this brief, a new constructive learning algorithm for feedforward neural net...
Network training algorithms have heavily concentrated on the learning of connection weights. Little ...
A positive reinforcement type learning algorithm is formulated for a stochastic feed-forward multila...
Neural Network models have received increased attention in the recent years. Aimed at achieving huma...
Reinforcement learning algorithms comprise a class of learning algorithms for neural networks. Reinf...
In this paper a review of fast-learning algorithms for multilayer neural networks is presented. From...
This paper demonstrates how a multi-layer feed-forward network may be trained, using the method of g...
A multilayer neural network based on multi-valued neurons is considered in the paper. A multivalued ...
In this paper we define on-line algorithms for neural-network training, based on the construction of...
Since the discovery of the back-propagation method, many modified and new algorithms have been propo...
An ongoing challenge in neural information processing is the following question: how do neurons adju...