Biomedical subjects

Janusz A Starzyk

Publications and source records attributed to Janusz A Starzyk.

3 recordsLinked to original sources

PubMed2006

Associative learning in hierarchical self-organizing learning arrays.

In this paper, we introduce feedback-based associative learning in self-organized learning arrays (SOLAR). SOLAR structures are hierarchically organized networks of sparsely connected neurons that define their own functions and select their interconnections locally. This paper provides a description of neuron self-organization and signal processing. Feedforward processing is used to make necessary correlations and learn the input patterns. Discovered associations between neuron inputs are used to generate feedback signals. These feedback signals, when propagated to the primary inputs, can establish the expected input values. This can be used for heteroassociative (HA) and autoassociative (AA) learning and pattern recognition. Example applications in HA learning are given.

Algorithms↗

PubMed2005

Self-organizing learning array.

A new machine learning concept--self-organizing learning array (SOLAR)--is presented. It is a sparsely connected, information theory-based learning machine, with a multilayer structure. It has reconfigurable processing units (neurons) and an evolvable system structure, which makes it an adaptive classification system for a variety of machine learning problems. Its multilayer structure can handle complex problems. Based on the entropy estimation, information theory-based learning is performed locally at each neuron. Neural parameters and connections that correspond to minimum entropy are adaptively set for each neuron. By choosing connections for each neuron, the system sets up its wiring and completes its self-organization. SOLAR classifies input data based on the weighted statistical information from all the neurons. The system classification ability has been simulated and experiments were conducted using test-bench data. Results show a very good performance compared to other classification methods. An important advantage of this structure is its scalability to a large system and ease of hardware implementation on regular arrays of cells.

Learning↗

PubMed2004

Dynamic probability estimator for machine learning.

An efficient algorithm for dynamic estimation of probabilities without division on unlimited number of input data is presented. The method estimates probabilities of the sampled data from the raw sample count, while keeping the total count value constant. Accuracy of the estimate depends on the counter size, rather than on the total number of data points. Estimator follows variations of the incoming data probability within a fixed window size, without explicit implementation of the windowing technique. Total design area is very small and all probabilities are estimated concurrently. Dynamic probability estimator was implemented using a programmable gate array from Xilinx. The performance of this implementation is evaluated in terms of the area efficiency and execution time. This method is suitable for the highly integrated design of artificial neural networks where a large number of dynamic probability estimators can work concurrently.

Artificial Intelligence↗