Higgins, C. M., & Goodman, R. M. (1994). Fuzzy rule-based networks for control. IEEE Transactions on Fuzzy Systems, 2(1), 82-88.
Abstract:
We present a method for learning fuzzy logic membership functions and rule to approximate a numerical function from a set of examples of the functions independent variables and the resulting function value. This method uses a three-step approach to building a complete function approximation system: first, learning the membership functions and creating a cell-based rule representation; second, simplifying the cell-based rules using an information-theoretic approach for induction of rules from discrete-valued data; and, finally, constructing a computational (neural) network to compute the function value given its independent variables. This function approximation system is demonstrated with a simple control example: learning the truck and the trailer backer-upper control system.
Higgins, C. M. (2001). Sensory architectures for biologically inspired autonomous robotics. Biological Bulletin, 200(2), 235-242.
PMID: 11341590;Abstract:
Engineers have a lot to gain from studying biology. The study of biological neural systems alone provides numerous examples of computational systems that are far more complex than any man-made system and perform real-time sensory and motor tasks in a manner that humbles the most advanced artificial systems. Despite the evolutionary genesis of these systems and the vast apparent differences between species, there are common design strategies employed by biological systems that span taxa, and engineers would do well to emulate these strategies. However, biologically-inspired computational architectures, which are continuous-time and parallel in nature, do not map well onto conventional processors, which are discrete-time and serial in operation. Rather, an implementation technology that is capable of directly realizing the layered parallel structure and nonlinear elements employed by neurobiology is required for power- and space-efficient implementation. Custom neuromorphic hardware meets these criteria and yields low-power dedicated sensory systems that are small, light, and ideal for autonomous robot applications. As examples of how this technology is applied, this article describes both a low-level neuromorphic hardware emulation of an elementary visual motion detector, and a large-scale, system-level spatial motion integration system.
Ozalevli, E., & Higgins, C. M. (2003). Multi-chip implementation of a biomimetic VLSI vision sensor based on the Adelson-Bergen algorithm. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2714, 433-440.
Abstract:
Biological motion sensors found in the retinas of species ranging from flies to primates are tuned to specific spatio-temporal frequencies to determine the local motion vectors in their visual field and perform complex motion computations. In this study, we present a novel implementation of a silicon retina based on the Adelson-Bergen spatio-temporal energy model of primate cortical cells. By employing a multi-chip strategy, we successfully implemented the model without much sacrifice of the fill factor of the photoreceptors in the front-end chip. In addition, the characterization results proved that this spatio-temporal frequency tuned silicon retina can detect the direction of motion of a sinusoidal input grating down to 10 percent contrast, and over more than a magnitude in velocity. This multi-chip biomimetic vision sensor will allow complex visual motion computations to be performed in real-time. © Springer-Verlag Berlin Heidelberg 2003.
Northcutt, B. D., & Higgins, C. M. (2018). A Minimal Computational Architecture for Range Estimation and Mapping. Robotics and Autonomous Systems.
Higgins, C. M., & Goodman, R. M. (1991). Incremental learning with rule-based neural networks. Proceedings. IJCNN-91-Seattle: International Joint Conference on Neural Networks, 875-880.
Abstract:
A classifier for discrete-valued variable classification problems is presented. The system utilizes an information-theoretic algorithm for constructing informative rules from example data. These rules are then used to construct a neural network to perform parallel inference and posterior probability estimation. The network can be grown incrementally, so that new data can be incorporated without repeating the training on previous data. It is shown that this technique performs as well as other techniques such as backpropagation while having unique advantages in incremental learning capability, training efficiency, knowledge representation, and hardware implementation suitability.
