Results 221 to 230 of about 27,765 (261)
Some of the next articles are maybe not open access.
Injecting Chaos in Feedforward Neural Networks
Neural Processing Letters, 2011Chaos appears in many natural and artificial systems; accordingly, we propose a method that injects chaos into a supervised feed forward neural network (NN). The chaos is injected simultaneously in the learnable temperature coefficient of the sigmoid activation function and in the weights of the NN. This is functionally different from the idea of noise
Sultan Uddin Ahmed +2 more
openaire +1 more source
A Multilayer Feedforward Fuzzy Neural Network
2006This paper describes the architecture and learning procedure of a multilayer feedforward fuzzy neural network (FNN). The FNN is designed by replacing the sigmoid type activation function of the multilayer neural network (NN) with the fuzzy system (FS). The Levenberg-Marquardt (LM) optimization method with a trust region approach is adapted to train the
openaire +2 more sources
Transmission of neural activity in a feedforward network
NeuroReport, 2005In this work, the enhancement of coherence resonance of firings in a 10-layer feedforward neuronal network with sparse couplings is found when there is noise input to each layer. Periodic signals with frequency 30-80 Hz are found to be well transmitted though the network, and such a frequency sensitivity can be modulated by the noise intensity and is ...
Sentao, Wang, Wei, Wang
openaire +2 more sources
Feedforward neural networks for automated classification
9th IEEE International Conference on Cognitive Informatics (ICCI'10), 2010We investigate an intelligent computer vision system that incorporates feedforward neural networks (NN) for recognition and classification of commercially available cork tiles. The system is capable of acquiring and processing gray images using several feature generation and analysis techniques.
Ivan Jordanov, Antoniya Georgieva
openaire +2 more sources
DYSLEXIC BEHAVIOUR OF FEEDFORWARD NEURAL NETWORKS
International Journal of Neural Systems, 1990A model is proposed in which the synaptic efficacies of a feedforward neural network are adapted with a cost function that vanishes if the boolean function that is represented has the same symmetry properties as the target one. The function chosen according to this procedure is thus taken as an archetype of the whole symmetry class.
Edgardo A. Ferrán +1 more
openaire +1 more source
A generalized feedforward neural network classifier
Proceedings of the International Joint Conference on Neural Networks, 2003., 2004In this article a new generalized feedforward neural network (GFNN) architecture for pattern classification is proposed. The GFNNs are an expansion of shunting inhibitory artificial neural networks (SIANNs), proposed previously for classification and function approximations.
Ganesh Arulampalam +1 more
openaire +2 more sources
An improved ARPSO for feedforward neural networks
2011 Seventh International Conference on Natural Computation, 2011Although particle swam optimization (PSO) algorithm is a good optimization tool for feedforward neural network s(FNN), it is easy to lose the diversity of the swarm and suffer from premature convergence. An improved PSO algorithm based on the attractive and repulsive PSO (ARPSO) is proposed to train FNN in this paper.
Fei Han 0001, Jiansheng Zhu
openaire +1 more source
Feedforward neural networks with random weights
Proceedings., 11th IAPR International Conference on Pattern Recognition. Vol.II. Conference B: Pattern Recognition Methodology and Systems, 2003In the field of neural network research a number of experiments described seem to be in contradiction with the classical pattern recognition or statistical estimation theory. The authors attempt to give some experimental understanding why this could be possible by showing that a large fraction of the parameters (the weights of neural networks) are of ...
Wouter F. Schmidt +2 more
openaire +1 more source
2000
As mentioned before, adaptation or learning is a major focus of neural network research. The Adaline,which will be introduced in this section, is the simplest artificial neural model with learning capability or adapting capability. The word Adaline (see [311]–[313]) is an abbreviation for adaptive linear neuron.
openaire +1 more source
As mentioned before, adaptation or learning is a major focus of neural network research. The Adaline,which will be introduced in this section, is the simplest artificial neural model with learning capability or adapting capability. The word Adaline (see [311]–[313]) is an abbreviation for adaptive linear neuron.
openaire +1 more source
Local coupled feedforward neural network
Neural Networks, 2010In this paper, the local coupled feedforward neural network is presented. Its connection structure is same as that of Multilayer Perceptron with one hidden layer. In the local coupled feedforward neural network, each hidden node is assigned an address in an input space, and each input activates only the hidden nodes near it.
exaly +4 more sources