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Neural Networks, IEEE Transactions on

Issue 2 • Date March 1991

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Displaying Results 1 - 19 of 19
  • Comments on "parallel algorithms for finding a near-maximum independent set of a circle graph" [with reply]

    Publication Year: 1991 , Page(s): 328 - 329
    Request Permissions | Click to expandAbstract | PDF file iconPDF (181 KB)  

    The authors refers to the work of Y. Takefuji et al. (see ibid., vol.1, pp. 263-267, Sept. (1990)), which is concerned with the problem of RNA secondary structure prediction, and draws the reader's attention to his own model and experiments in training the neural networks on small tRNA subsequences. The author admits that Takefuji et al. outline an elegant way to map the problem onto neural archit... View full abstract»

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  • Orthogonal least squares learning algorithm for radial basis function networks

    Publication Year: 1991 , Page(s): 302 - 309
    Cited by:  Papers (784)  |  Patents (7)
    Request Permissions | Click to expandAbstract | PDF file iconPDF (580 KB)  

    The radial basis function network offers a viable alternative to the two-layer neural network in many applications of signal processing. A common learning algorithm for radial basis function networks is based on first choosing randomly some data points as radial basis function centers and then using singular-value decomposition to solve for the weights of the network. Such a procedure has several ... View full abstract»

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  • Performance and generalization of the classification figure of merit criterion function

    Publication Year: 1991 , Page(s): 322 - 325
    Cited by:  Papers (5)
    Request Permissions | Click to expandAbstract | PDF file iconPDF (384 KB)  

    A criterion function-the classification figure of merit (CFM)-for training neural networks, introduced by J.B. Hampshire and A.H. Waibel (IEEE Trans. Neural Networks, vol. 1, pp. 216-218, June (1990)), is studied. It is shown that this criterion function has some highly desirable properties. CFM has optimal training-set performance, which is related (but not equivalent) to its monotonicity. Howeve... View full abstract»

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  • Multiscale optimization in neural nets

    Publication Year: 1991 , Page(s): 263 - 274
    Cited by:  Papers (4)  |  Patents (1)
    Request Permissions | Click to expandAbstract | PDF file iconPDF (1000 KB)  

    One way to speed up convergence in a large optimization problem is to introduce a smaller, approximate version of the problem at a coarser scale and to alternate between relaxation steps for the fine-scale and coarse-scale problems. Such an optimization method for neural networks governed by quite general objective functions is presented. At the coarse scale, there is a smaller approximating neura... View full abstract»

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  • A simple neuron servo

    Publication Year: 1991 , Page(s): 248 - 251
    Cited by:  Papers (8)
    Request Permissions | Click to expandAbstract | PDF file iconPDF (316 KB)  

    A simple servo controller built from components having neuronlike features is described. This VLSI servo controller uses pulses for control and is orders of magnitude smaller than a conventional system. The basic circuit elements are described. A key element is a component and neuronlike capability that takes voltages as inputs and generates a pulse train as the output. It is shown how the circuit... View full abstract»

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  • Current-mode subthreshold MOS circuits for analog VLSI neural systems

    Publication Year: 1991 , Page(s): 205 - 213
    Cited by:  Papers (137)  |  Patents (3)
    Request Permissions | Click to expandAbstract | PDF file iconPDF (916 KB)  

    An overview of the current-mode approach for designing analog VLSI neural systems in subthreshold CMOS technology is presented. Emphasis is given to design techniques at the device level using the current-controlled current conveyor and the translinear principle. Circuits for associative memory and silicon retina systems are used as examples. The design methodology and how it relates to actual bio... View full abstract»

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  • A real-time neural system for color constancy

    Publication Year: 1991 , Page(s): 237 - 247
    Cited by:  Papers (31)  |  Patents (13)
    Request Permissions | Click to expandAbstract | PDF file iconPDF (1148 KB)  

    A neural network approach to the problem of color constancy is presented. Various algorithms based on Land's retinex theory are discussed with respect to neurobiological parallels, computational efficiency, and suitability for VLSI implementation. The efficiency of one algorithm is improved by the application of resistive grids and is tested in computer simulations; the simulations make clear the ... View full abstract»

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  • K-winner networks

    Publication Year: 1991 , Page(s): 310 - 315
    Cited by:  Papers (45)
    Request Permissions | Click to expandAbstract | PDF file iconPDF (456 KB)  

    A special class of mutually inhibitory networks is analyzed, and parameters for reliable K-winner performance are presented. The network dynamics are modeled using interactive activation, and results are compared with the sigmoid model. For equal external inputs, network parameters that select the units with the larger initial activations (the network converges to the nearest stable state... View full abstract»

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  • Recurrent correlation associative memories

    Publication Year: 1991 , Page(s): 275 - 284
    Cited by:  Papers (49)
    Request Permissions | Click to expandAbstract | PDF file iconPDF (728 KB)  

    A model for a class of high-capacity associative memories is presented. Since they are based on two-layer recurrent neural networks and their operations depend on the correlation measure, these associative memories are called recurrent correlation associative memories (RCAMs). The RCAMs are shown to be asymptotically stable in both synchronous and asynchronous (sequential) update modes as long as ... View full abstract»

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  • Analog VLSI model of binaural hearing

    Publication Year: 1991 , Page(s): 230 - 236
    Cited by:  Papers (21)  |  Patents (2)
    Request Permissions | Click to expandAbstract | PDF file iconPDF (860 KB)  

    The stereausis model of biological auditory processing is proposed as a representation that encodes both binaural and spectral information in a unified framework. A working analog VLSI chip that implements this model of early auditory processing in the brain is described. The chip is a 100000-transistor integrated circuit that computes the stereausis representation in real time, using continuous-t... View full abstract»

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  • VLSI implementation of ART1 memories

    Publication Year: 1991 , Page(s): 214 - 221
    Cited by:  Papers (16)
    Request Permissions | Click to expandAbstract | PDF file iconPDF (548 KB)  

    A hardware implementation of long-term memory and short-term memory for binary input adaptive resonance theory (ART1) neural networks is presented. This implementation is based on chemical-electrical interactions in real neurons which are known to control axon release of chemical materials which in turn modulate the conductances of synapses. An axon-synapse-tree structure is introduced to achieve ... View full abstract»

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  • Gradient methods for the optimization of dynamical systems containing neural networks

    Publication Year: 1991 , Page(s): 252 - 262
    Cited by:  Papers (227)  |  Patents (3)
    Request Permissions | Click to expandAbstract | PDF file iconPDF (940 KB)  

    An extension of the backpropagation method, termed dynamic backpropagation, which can be applied in a straightforward manner for the optimization of the weights (parameters) of multilayer neural networks is discussed. The method is based on the fact that gradient methods used in linear dynamical systems can be combined with backpropagation methods for neural networks to obtain the gradient of a pe... View full abstract»

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  • Robust stability analysis of adaptation algorithms for single perceptron

    Publication Year: 1991 , Page(s): 325 - 328
    Cited by:  Papers (2)
    Request Permissions | Click to expandAbstract | PDF file iconPDF (264 KB)  

    The problem of robust stability and convergence of learning parameters of adaptation algorithms in a noisy environment for the single preceptron is addressed. The case in which the same input pattern is presented in the adaptation cycle is analyzed. The algorithm proposed is of the Widrow-Hoff type. It is concluded that this algorithm is robust. However, the weight vectors do not necessarily conve... View full abstract»

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  • A programmable analog neural network processor

    Publication Year: 1991 , Page(s): 222 - 229
    Cited by:  Papers (10)  |  Patents (1)
    Request Permissions | Click to expandAbstract | PDF file iconPDF (896 KB)  

    An analog neural network breadboard consisting of 256 neurons and 2048 programmable synaptic weights of 5 bits each is constructed and tested. The heart of the processor is an array of custom-programmable synapse (resistor) chips on a reconfigurable neuron board. The analog bandwidth of the system is 90 kHz. The breadboard is used to demonstrate the application of neural network learning to the pr... View full abstract»

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  • Pulse-stream VLSI neural networks mixing analog and digital techniques

    Publication Year: 1991 , Page(s): 193 - 204
    Cited by:  Papers (98)  |  Patents (9)
    Request Permissions | Click to expandAbstract | PDF file iconPDF (1148 KB)  

    The pulse-stream technique, which represents neural states as sequences of pulses, is reviewed. Several general issues are raised, and generic methods appraised, for pulsed encoding, arithmetic, and intercommunication schemes. Two contrasting synapse designs are presented and compared. The first is based on a fully analog computational form in which the only digital component is the signaling mech... View full abstract»

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  • Neural networks-then and now

    Publication Year: 1991 , Page(s): 316 - 318
    Cited by:  Papers (2)  |  Patents (3)
    Request Permissions | Click to expandAbstract | PDF file iconPDF (344 KB)  

    The emergence of neural networks as a significant subdiscipline with corresponding attempts at application to engineering problems is traced back to the 1960s, when Frank Rosenblatt, a Cornell University psychologist, showed by mathematical analysis, digital computer simulation, and experiments with special-purpose parallel analog systems that neural networks with variable-weight connections could... View full abstract»

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  • Adaptive nearest neighbor pattern classification

    Publication Year: 1991 , Page(s): 318 - 322
    Cited by:  Papers (26)  |  Patents (3)
    Request Permissions | Click to expandAbstract | PDF file iconPDF (436 KB)  

    A variant of nearest-neighbor (NN) pattern classification and supervised learning by learning vector quantization (LVQ) is described. The decision surface mapping method (DSM) is a fast supervised learning algorithm and is a member of the LVQ family of algorithms. A relatively small number of prototypes are selected from a training set of correctly classified samples. The training set is then used... View full abstract»

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  • Neuromorphic learning of continuous-valued mappings from noise-corrupted data

    Publication Year: 1991 , Page(s): 294 - 301
    Cited by:  Papers (2)
    Request Permissions | Click to expandAbstract | PDF file iconPDF (728 KB)  

    The effect of noise on the learning performance of the backpropagation algorithm is analyzed. A selective sampling of the training set is proposed to maximize the learning of control laws by backpropagation, when the data have been corrupted by noise. The training scheme is applied to the nonlinear control of a cart-pole system in the presence of noise. The neural computation provides the neurocon... View full abstract»

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  • A tree-structured adaptive network for function approximation in high-dimensional spaces

    Publication Year: 1991 , Page(s): 285 - 293
    Cited by:  Papers (37)
    Request Permissions | Click to expandAbstract | PDF file iconPDF (1164 KB)  

    Nonlinear function approximation is often solved by finding a set of coefficients for a finite number of fixed nonlinear basis functions. However, if the input data are drawn from a high-dimensional space, the number of required basis functions grows exponentially with dimension, leading many to suggest the use of adaptive nonlinear basis functions whose parameters can be determined by iterative m... View full abstract»

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Aims & Scope

IEEE Transactions on Neural Networks is devoted to the science and technology of neural networks, which disclose significant technical knowledge, exploratory developments, and applications of neural networks from biology to software to hardware.

 

This Transactions ceased production in 2011. The current retitled publication is IEEE Transactions on Neural Networks and Learning Systems.

Full Aims & Scope