Search PubMed⌕ Search

Biomedical subjects

P Saratchandran

Publications and source records attributed to P Saratchandran.

4 recordsLinked to original sources
PubMed1997

A sequential learning scheme for function approximation using minimal radial basis function neural networks.

This article presents a sequential learning algorithm for function approximation and time-series prediction using a minimal radial basis function neural network (RBFNN). The algorithm combines the growth criterion of the resource-allocating network (RAN) of Platt (1991) with a pruning strategy based on the relative contribution of each hidden unit to the overall network output. The resulting network leads toward a minimal topology for the RBFNN. The performance of the algorithm is compared with RAN and the enhanced RAN algorithm of Kadirkamanathan and Niranjan (1993) for the following benchmark problems: (1) hearta from the benchmark problems database PROBEN1, (2) Hermite polynomial, and (3) Mackey-Glass chaotic time series. For these problems, the proposed algorithm is shown to realize RBFNNs with far fewer hidden neurons with better or same accuracy.

Algorithms↗
PubMed1995

Analysis of training set parallelism for backpropagation neural networks.

Training set parallelism and network based parallelism are two popular paradigms for parallelizing a feedforward (artificial) neural network. Training set parallelism is particularly suited to feedforward neural networks with backpropagation learning where the size of the training set is large in relation to the size of the network. This paper analyzes training set parallelism for feedforward neural networks when implemented on a transputer array configured in a pipelined ring topology. Theoretical expressions for the time per epoch (iteration) and optimal size of a processor network are derived when the training set is equally distributed among the processing nodes. These show that the speed up is a function of the number of patterns per processor, communication overhead per epoch and the total number of processors in the topology. Further analysis of how to optimally distribute the training set on a given processor network when the number of patterns in the training set is not an integer multiple of the number of processors, is also carried out. It is shown that optimal allocation of patterns in such cases is a mixed integer programming problem. Using this analysis it is found that equal distribution of training patterns among the processors is not the optimal way to allocate the patterns even when the training set is an integer multiple of the number of processors. Extension of the analysis to processor networks comprising processors of different speeds is also carried out. Experimental results from a T805 transputer array are presented to verify all the theoretical results.

Algorithms↗
PubMed1976

An improved mathematical model of human thyroid hormone regulation.

1. A mathematical model has been constructed of human thyroid hormone regulation by the anterior pituitary gland, which takes account of most of the currently available experimental data. 2. Successful simulation of data on the stimulation of thyrotrophin (TSH) secretion by thyrotrophin releasing hormone (TRH) was achieved assuming that the TSH secretion rate is proportional to the logarithm of the concurrent blood TRH level. 3. Data on the regulation of triiodothyronine (T3) secretion by TSH and the inhibition of TSH secretion by thyroid hormones in contrast could not be simulated on the assumption of instantaneous proportional responses. A mixture of proportional and integral control--the latter taking account of the past history of plasma levels of the regulatory hormone--appeared to be operating at both levels. 4. The pituitary gland appears to be more sensitive to a given fractional change in TRH secretion rate than to the same fractional change in T3 plasma concentration.

Humans↗