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Scanning the issue
NEUROCOMPUTINC Neurocomputing 14 (1997) 1
Scanning the issue A. Datta, T. Pal, and SK. Parui describe A modified self-organizing neural net for shape extraction. Skeletons of binary images are extracted by a variation of the Kohonen’s model, which allows that the set of neurons and their neighborhoods adaptively change during learning as opposed to remaining fixed as in the original model. In Assigning meaning to data: Using sparse distributed memory for multilevel cognitive tasks L.M. Manevitz and Y. Zemach show running experiments in a unified memory retrieval system with the use of a single homogeneous SDM memory for multilevel infonnation processing. One- and two-way language (English, Hebrew) translation were implemented. U. Sandler proposes A neural network with multi-neurons. The state of a multi-neuron is desbribed by a parameter set. The resulting neural network is capable to distinguish correlated patterns. The model description, the network dynamics in the absence of internal noise, learning rules, network thermodynamics, dynamics at finite temperature, the simulation results, and a multimode laser as hardware implementation of the multi-neuron are discussed. In High pe@ormance training of feedforward and simple recurrent networks B.L. Kalman and S.C. Kwasny describe a set of training methods and tools for the design and training of large and complicated networks. It is integrated in the TRAINRBC system, which is based on epoch-based training and uses the conjugate gradient algorithm. In the case of recurrent neural networks and recursive auto associative memories architecturespecific concepts are incorporated. G. Bloch, P. Thomas, and D. Theilliol present a new algorithm for the Accomodation to outliers in identification of nonlinear SISO systems with neural networks. A recursive prediction error method, its robustification, and simulation results are discussed. Potential applications include identification from contaminated data, failure detection, and robust control. I appreciate the cooperation of all those who submitted their work for inclusion in this issue. V. David Sfinchez A. Editor-in-Chief 0925~2312/97/$17.00 0 1997 Elsevier Science B.V. All rights reserved
Scanning the issue A. Datta, T. Pal, and SK. Parui describe A modified self-organizing neural net for shape extraction. Skeletons of binary images are extracted by a variation of the Kohonen’s model, which allows that the set of neurons and their neighborhoods adaptively change during learning as opposed to remaining fixed as in the original model. In Assigning meaning to data: Using sparse distributed memory for multilevel cognitive tasks L.M. Manevitz and Y. Zemach show running experiments in a unified memory retrieval system with the use of a single homogeneous SDM memory for multilevel infonnation processing. One- and two-way language (English, Hebrew) translation were implemented. U. Sandler proposes A neural network with multi-neurons. The state of a multi-neuron is desbribed by a parameter set. The resulting neural network is capable to distinguish correlated patterns. The model description, the network dynamics in the absence of internal noise, learning rules, network thermodynamics, dynamics at finite temperature, the simulation results, and a multimode laser as hardware implementation of the multi-neuron are discussed. In High pe@ormance training of feedforward and simple recurrent networks B.L. Kalman and S.C. Kwasny describe a set of training methods and tools for the design and training of large and complicated networks. It is integrated in the TRAINRBC system, which is based on epoch-based training and uses the conjugate gradient algorithm. In the case of recurrent neural networks and recursive auto associative memories architecturespecific concepts are incorporated. G. Bloch, P. Thomas, and D. Theilliol present a new algorithm for the Accomodation to outliers in identification of nonlinear SISO systems with neural networks. A recursive prediction error method, its robustification, and simulation results are discussed. Potential applications include identification from contaminated data, failure detection, and robust control. I appreciate the cooperation of all those who submitted their work for inclusion in this issue. V. David Sfinchez A. Editor-in-Chief 0925~2312/97/$17.00 0 1997 Elsevier Science B.V. All rights reserved