Biomedical subjects

H C Card

Publications and source records attributed to H C Card.

4 recordsLinked to original sources

PubMed2001

Stochastic radial basis functions.

Stochastic signal processing can implement gaussian activation functions for radial basis function networks, using stochastic counters. The statistics of neural inputs which control the increment and decrement operations of the counter are governed by Bernoulli distributions. The transfer functions relating the input and output pulse probabilities can closely approximate gaussian activation functions which improve with the number of states in the counter. The means and variances of these gaussian approximations can be controlled by varying the output combinational logic function of the binary counter variables.

Binomial Distribution↗

PubMed2001

An investigation of competitive learning for autonomous cluster identification in embedded systems.

Robust signal processing for embedded systems requires the effective identification and representation of features within raw sensory data. This task is inherently difficult due to unavoidable long-term changes in the sensory systems and/or the sensed environment. In this paper we explore four variations of competitive learning and examine their suitability as an unsupervised technique for the automated identification of data clusters within a given input space. The relative performance of the four techniques is evaluated through their ability to effectively represent the structure underlying artificial and real-world data distributions. As a result of this study it was found that frequency sensitive competitive learning provides both reliable and efficient solutions to complex data distributions. As well, frequency sensitive and soft competitive learning are shown to exhibit properties which may permit the evolution of an appropriate network structure through the use of growing or pruning procedures.

Cluster Analysis↗

PubMed2000

Cooperative coevolution of neural representations.

A genetic algorithm (GA) is used to search for a set of local feature detectors or hidden units. These are in turn employed as a representation of the input data for neural learning in the upper layer of a multilayer perceptron (MLP) which performs an image classification task. Three different methods of encoding hidden unit weights in the chromosome of the GA are presented, including one which coevolves all the feature detectors in a single chromosome, and two which promote the cooperation of feature detectors by encoding them in their own individual chromosomes. The fitness function measures the MLP classification accuracy together with the confidence of the networks.

Algorithms↗

PubMed2000

Instabilities and oscillation in the deterministic Boltzmann machine.

Simulations indicate that the deterministic Boltzmann machine, unlike the stochastic Boltzmann machine from which it is derived, exhibits unstable behavior during contrastive Hebbian learning of nonlinear problems, including oscillation in the learning algorithm and extreme sensitivity to small weight perturbations. Although careful choice of the initial weight magnitudes, the learning rate, and the annealing schedule will produce convergence in most cases, the stability of the resulting solution depends on the parameters in a complex and generally indiscernible way. We show that this unstable behavior is the result of over parameterization (excessive freedom in the weights), which leads to continuous rather than isolated optimal weight solution sets. This allows the weights to drift without correction by the learning algorithm until the free energy landscape changes in such a way that the settling procedure employed finds a different minimum of the free energy function than it did previously and a gross output error occurs. Because all the weight sets in a continuous optimal solution set produce exactly the same network outputs, we define reliability, a measure of the robustness of the network, as a new performance criterion.

Algorithms↗