Article records
https://feeds.library.caltech.edu/people/Higgins-C-M/article.rss
A Caltech Library Repository Feedhttp://www.rssboard.org/rss-specificationpython-feedgenenTue, 16 Apr 2024 13:42:10 +0000Rule-based neural networks for classification and probability estimation
https://resolver.caltech.edu/CaltechAUTHORS:GOOnc92
Authors: {'items': [{'id': 'Goodman-R-M', 'name': {'family': 'Goodman', 'given': 'Rodney M.'}}, {'id': 'Higgins-C-M', 'name': {'family': 'Higgins', 'given': 'Charles M.'}}, {'id': 'Miller-J-W', 'name': {'family': 'Miller', 'given': 'John W.'}}, {'id': 'Smyth-P', 'name': {'family': 'Smyth', 'given': 'Padhraic'}}]}
Year: 1992
DOI: 10.1162/neco.1992.4.6.781
In this paper we propose a network architecture that combines a rule-based approach with that of the neural network paradigm. Our primary motivation for this is to ensure that the knowledge embodied in the network is explicitly encoded in the form of understandable rules. This enables the network's decision to be understood, and provides an audit trail of how that decision was arrived at. We utilize an information theoretic approach to learning a model of the domain knowledge from examples. This model takes the form of a set of probabilistic conjunctive rules between discrete input evidence variables and output class variables. These rules are then mapped onto the weights and nodes of a feedforward neural network resulting in a directly specified architecture. The network acts as parallel Bayesian classifier, but more importantly, can also output posterior probability estimates of the class variables. Empirical tests on a number of data sets show that the rule-based classifier performs comparably with standard neural network classifiers, while possessing unique advantages in terms of knowledge representation and probability estimation.https://authors.library.caltech.edu/records/w6w9f-eaa23Fuzzy rule-based networks for control
https://resolver.caltech.edu/CaltechAUTHORS:20190315-142400048
Authors: {'items': [{'id': 'Higgins-C-M', 'name': {'family': 'Higgins', 'given': 'Charles M.'}}, {'id': 'Goodman-R-M', 'name': {'family': 'Goodman', 'given': 'Rodney M.'}}]}
Year: 1994
DOI: 10.1109/91.273129
We present a method for learning fuzzy logic membership functions and rules to approximate a numerical function from a set of examples of the function's 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 trailer backer-upper control system.https://authors.library.caltech.edu/records/ad2ma-5wg55A Modular Multi-Chip Neuromorphic Architecture for Real-Time Visual Motion Processing
https://resolver.caltech.edu/CaltechAUTHORS:20130816-103146617
Authors: {'items': [{'id': 'Higgins-C-M', 'name': {'family': 'Higgins', 'given': 'Charles M.'}}, {'id': 'Koch-C', 'name': {'family': 'Koch', 'given': 'Christof'}, 'orcid': '0000-0001-6482-8067'}]}
Year: 2000
DOI: 10.1023/A:1008309524326
The extent of pixel-parallel focal plane image processing is limited by pixel area and imager fill factor. In this paper, we describe a novel multi-chip neuromorphic VLSI visual motion processing system which combines analog circuitry with an asynchronous digital interchip communications protocol to allow more complex pixel-parallel motion processing than is possible in the focal plane. This multi-chip system retains the primary advantages of focal plane neuromorphic image processors: low-power consumption, continuous-time operation, and small size. The two basic VLSI building blocks are a photosensitive sender chip which incorporates a 2D imager array and transmits the position of moving spatial edges, and a receiver chip which computes a 2D optical flow vector field from the edge information. The elementary two-chip motion processing system consisting of a single sender and receiver is first characterized. Subsequently, two three-chip motion processing systems are described. The first three-chip system uses two sender chips to compute the presence of motion only at a particular stereoscopic depth from the imagers. The second three-chip system uses two receivers to simultaneously compute a linear and polar topographic mapping of the image plane, resulting in information about image translation, rotation, and expansion. These three-chip systems demonstrate the modularity and flexibility of the multi-chip neuromorphic approach.https://authors.library.caltech.edu/records/8dpek-e8146