Real-time modeling of primitive environments through wavelet sensors and Hebbian learning

James M. Vaccaro; Paul S. Yaworsky

doi:10.1117/12.351188

22 June 1999 Real-time modeling of primitive environments through wavelet sensors and Hebbian learning

James M. Vaccaro, Paul S. Yaworsky

Proceedings Volume 3696, Enabling Technology for Simulation Science III; (1999) https://doi.org/10.1117/12.351188
Event: AeroSense '99, 1999, Orlando, FL, United States

Abstract

Modeling the world through sensory input necessarily provides a unique perspective for the observer. Given a limited perspective, objects and events cannot always be encoded precisely but must involve crude, quick approximations to deal with sensory information in a real- time manner. As an example, when avoiding an oncoming car, a pedestrian needs to identify the fact that a car is approaching before ascertaining the model or color of the vehicle. In our methodology, we use wavelet-based sensors with self-organized learning to encode basic sensory information in real-time. The wavelet-based sensors provide necessary transformations while a rank-based Hebbian learning scheme encodes a self-organized environment through translation, scale and orientation invariant sensors. Such a self-organized environment is made possible by combining wavelet sets which are orthonormal, log-scale with linear orientation and have automatically generated membership functions. In earlier work we used Gabor wavelet filters, rank-based Hebbian learning and an exponential modulation function to encode textural information from images. Many different types of modulation are possible, but based on biological findings the exponential modulation function provided a good approximation of first spike coding of `integrate and fire' neurons. These types of Hebbian encoding schemes (e.g., exponential modulation, etc.) are useful for quick response and learning, provide several advantages over contemporary neural network learning approaches, and have been found to quantize data nonlinearly. By combining wavelets with Hebbian learning we can provide a real-time front-end for modeling an intelligent process, such as the autonomous control of agents in a simulated environment.

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James M. Vaccaro and Paul S. Yaworsky "Real-time modeling of primitive environments through wavelet sensors and Hebbian learning", Proc. SPIE 3696, Enabling Technology for Simulation Science III, (22 June 1999); https://doi.org/10.1117/12.351188

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KEYWORDS

Sensors

Wavelets

Environmental sensing

Visualization

Modulation

Information visualization

Process modeling

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