Self-Organizing Map Neural Architectures Based on Limit Cycle Attractors

Recent efforts to develop large-scale neural architectures have paid relatively little attention to the use of self-organizing maps (SOMs). Part of the reason is that most conventional SOMs use a static encoding representation: Each input is typically represented by the fixed activation of a single node in the map layer. This not only carries information in an inefficient and unreliable way that impedes building robust multi-SOM neural architectures, but it is also inconsistent with rhythmic oscillations in biological neural networks. Here I develop and study an alternative encoding scheme that instead uses limit cycle attractors of multi-focal activity patterns to represent input patterns/sequences. Such a fundamental change in representation raises several questions: Can this be done effectively and reliably? If so, will map formation still occur? What properties would limit cycle SOMs exhibit? Could multiple such SOMs interact effectively? Could robust architectures based on such SOMs be built for practical applications? The principal results of examining these questions are as follows. First, conditions are established for limit cycle attractors to emerge in a SOM through self-organization when encoding both static and temporal sequence inputs. It is found that under appropriate conditions a set of learned limit cycles are stable, unique, and preserve input relationships. In spite of the continually changing activity in a limit cycle SOM, map formation continues to occur reliably. Next, associations between limit cycles in different SOMs are learned. It is shown that limit cycles in one SOM can be successfully retrieved by another SOM’s limit cycle activity. Control timings can be set quite arbitrarily during both training and activation. Importantly, the learned associations generalize to new inputs that have never been seen during training. Finally, a complete neural architecture based on multiple limit cycle SOMs is presented for robotic arm control. This architecture combines open-loop and closed-loop methods to achieve high accuracy and fast movements through smooth trajectories. The architecture is robust in that disrupting or damaging the system in a variety of ways does not completely destroy the system. I conclude that limit cycle SOMs have great potentials for use in constructing robust neural architectures.

Mechanistic-based Design-Integrated Reliability Validation Framework for Mechanical Systems

Abstract: New product design challenges, related to customer needs, product usage and environments, face companies when they expand their product offerings to new markets; Some of the main challenges are: the lack of quantifiable information, product experience and field data. Designing reliable products under such challenges requires flexible reliability assessment processes that can capture the variables and parameters affecting the product overall reliability and allow different design scenarios to be assessed. These challenges also suggest a mechanistic (Physics of Failure-PoF) reliability approach would be a suitable framework to be used for reliability assessment. Mechanistic Reliability recognizes the primary factors affecting design reliability. This research views the designed entity as a “system of components required to deliver specific operations”; it addresses the above mentioned challenges by; Firstly: developing a design synthesis that allows a descriptive operations/ system components relationships to be realized; Secondly: developing component’s mathematical damage models that evaluate components Time to Failure (TTF) distributions given: 1) the descriptive design model, 2) customer usage knowledge and 3) design material properties; Lastly: developing a procedure that integrates components’ damage models to assess the mechanical system’s reliability over time. Analytical and numerical simulation models were developed to capture the relationships between operations and components, the mathematical damage models and the assessment of system’s reliability. The process was able to affect the design form during the conceptual design phase by providing stress goals to meet component’s reliability target. The process was able to numerically assess the reliability of a system based on component’s mechanistic TTF distributions, besides affecting the design of the component during the design embodiment phase. The process was used to assess the reliability of an internal combustion engine manifold during design phase; results were compared to reliability field data and found to produce conservative reliability results. The research focused on mechanical systems, affected by independent mechanical failure mechanisms that are influenced by the design process. Assembly and manufacturing stresses and defects’ influences are not a focus of this research.