Antoniou, G, Courtney, A, Ernst, J & Williams, M 1996, 'A System For Computing Constrained Default Logic Extensions', Lecture Notes in Computer Science, vol. 1126, pp. 237-250.
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The aim of this paper is to describe the algorithmic foundations of the part of the program Exten responsible for the computation of extensions in Constrained Default Logic. Exten is a system that computes extensions for various default logics. The effic
Finn, GD, Lister, R, Szabo, T, Simonetta, D, Mulder, H & Young, R 1996, 'Neural networks applied to a large biological database to analyse dairy breeding patterns', Neural Computing and Applications, vol. 4, no. 4, pp. 237-253.
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The paper recounts the investigation of a dairy sire prediction capability based on Cascade Correlation neural networks to study influences relating the performance of offspring to their parents. The context of the problem is the artificial insemination breeding program for the Australian dairy industry. The networks are used to screen observed information in the database to relate it to best combinations of dam and sire. The voluminous data is quite noisy and is subject to genetic and environmental influences. The intention is to extract linear and nonlinear relationships from among the input variables without specifying their form. A number of scenarios are employed which recast the data into different forms. In particular, it was discovered that the problem could be restructured and the data supplemented with transformed data to produce succinct input patterns of manageable dimensionality, which allowed for a substantially improved predictive capability. It was then found that reasonable daughter predictions could be obtained of about 10%, as measured by her milk production. Results are compared with those obtained using two alternate neural network methods. Crude statistical methods are employed to evaluate the performance of the neural networks.
Fowell, S & Levy, P 1996, 'Special issue on networked learner support', EDUCATION FOR INFORMATION, vol. 14, no. 4, pp. 257-260.
Jay, B & Staples, J 1996, 'Theoretical computer science in Australia and New Zealand - Preface', THEORETICAL COMPUTER SCIENCE, vol. 169, no. 1, pp. 1-1.
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Jay, CB 1996, 'Shape in computing', ACM Computing Surveys, vol. 28, pp. 355-357.
Jay, CB & Staples, J 1996, 'Preface to a special issue on Australasian research', Theoretical Computer Science, vol. 169.
Juang, CF & Lin, CT 1996, 'Self-organizing neural fuzzy inference network for indentification and control', Journal of Control Systems and Technology, vol. 4, no. 4, pp. 269-280.
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A Self-Organizing Neural Fuzzy Inference Network (SONFIN) with on-line learning ability is proposed in this paper. The SONFIN in inherently a fuzzy rule-based model possessing neural network's learning ability. In contrast to the general adaptive neural fuzzy networks, where the rules should be decided in advance before parameter learning is performed, there are no rules initially in the SONFIN. They are created and adapted as on-line learning proceeds via simultaneous structure and parameter identification. In this structure identification of the precondition part, the input space is partitioned in a flexible way according to an aligned clustering-based algorithm. As to the structure identification of the consequent part, only a singleton value selected by a clustering method is assigned to each rule initially. Afterwards, some additional terms (a linear combination of input variables) are added to the consequent part when necessary. Furthermore, to enhance the knowledge representation ability of the SONFIN, a linear transformation for each input variable can be incorporated into the network so that much fewer rules are needed or higher accuracy can be achieved. Proper linear transformation are also learned dynamically in the parameter identification phase of the SONFIN. To demonstrate the capability of the proposed SONFIN, simulations in identification and control problems are done. Effectiveness of the SONFIN is verified from these simulations.
Levy, P, Fowell, SP, Bowskill, N & Worsfold, E 1996, 'NetLinkS: A national professional development project for networked learner support', EDUCATION FOR INFORMATION, vol. 14, no. 4, pp. 261-278.
Lin, CJ & Lin, CT 1996, 'Reinforcement learning for an ART-based fuzzy adaptive learning control network.', IEEE transactions on neural networks, vol. 7, no. 3, pp. 709-731.
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This paper proposes a reinforcement fuzzy adaptive learning control network (RFALCON), constructed by integrating two fuzzy adaptive learning control networks (FALCON), each of which has a feedforward multilayer network and is developed for the realization of a fuzzy controller. One FALCON performs as a critic network (fuzzy predictor), the other as an action network (fuzzy controller). Using temporal difference prediction, the critic network can predict the external reinforcement signal and provide a more informative internal reinforcement signal to the action network. The action network performs a stochastic exploratory algorithm to adapt itself according to the internal reinforcement signal. An ART-based reinforcement structure/parameter-learning algorithm is developed for constructing the RFALCON dynamically. During the learning process, structure and parameter learning are performed simultaneously. RFALCON can construct a fuzzy control system through a reward/penalty signal. It has two important features; it reduces the combinatorial demands of system adaptive linearization, and it is highly autonomous.
Lin, CT 1996, 'Adaptive subset hood for neural fuzzy control', International Journal of Systems Science, vol. 27, no. 10, pp. 937-955.
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The paper extends Kosko’s fuzzy measure of subsethood (Kosko 1992) to a measure of mutual subsethood or fuzzyequivalence. Gaussian or bell-shaped fuzzy sets thensimplify the new measure and allow supervised learning to learn and tune the fuzzy rules. The gaussian sets act as nodes in neural fuzzy control networks and give a simple closed formfor the measure of mutual subsethood. The new adaptive subsethood controller (ASC) system uses the network structure to store, learn, and tune fuzzy rules. Simulations show how the ASC system can control a model car, balance an invertedwedge, and control the ball and beam system. © 1996 Taylor & Francis Group, LLC.
Lin, CT & Lu, YC 1996, 'A neural fuzzy system with fuzzy supervised learning.', IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society, vol. 26, no. 5, pp. 744-763.
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A neural fuzzy system learning with fuzzy training data (fuzzy if-then rules) is proposed in this paper. This system is able to process and learn numerical information as well as linguistic information. At first, we propose a five-layered neural network for the connectionist realization of a fuzzy inference system. The connectionist structure can house fuzzy logic rules and membership functions for fuzzy inference. We use alpha-level sets of fuzzy numbers to represent linguistic information. The inputs, outputs, and weights of the proposed network can be fuzzy numbers of any shape. Furthermore, they can be hybrid of fuzzy numbers and numerical numbers through the use of fuzzy singletons. Based on interval arithmetics, a fuzzy supervised learning algorithm is developed for the proposed system. It extends the normal supervised learning techniques to the learning problems where only linguistic teaching signals are available. The fuzzy supervised learning scheme can train the proposed system with desired fuzzy input-output pairs which are fuzzy numbers instead of the normal numerical values. With fuzzy supervised learning, the proposed system can be used for rule base concentration to reduce the number of rules in a fuzzy rule base. Simulation results are presented to illustrate the performance and applicability of the proposed system.
Lin, CT, Kan, MC & Chung, IF 1996, 'A neural network that learns from fuzzy data for language acquisition', International Journal of Uncertainty, Fuzziness and Knowlege-Based Systems, vol. 4, no. 6, pp. 581-603.
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This paper proposes a four-layered fuzzy language acquisition network (FLAN) for acquiring fuzzy language. It can catch the intended information from a sentence (command) spoken in natural language with fuzzy terms. The intended information includes a meaningful semantic action and the fuzzy linguistic information of that action (for example, the phrase "move forward" represents the meaningful semantic action and the phrase "very high speed" represents the linguistic information in the fuzzy command "Move forward in a very high speed."). The proposed FLAN has two important features. First, we can make no restrictions whatever on the fuzzy language input which is used to specify the desired information, and the network requires no acoustic, prosodic, grammar and syntactic structure. Second, the linguistic information of an action is learned automatically and it is represented by fuzzy numbers based on α-level sets. A supervised learning scheme is proposed to train the FLAN on fuzzy training data. This learning scheme consists of the mutual-information (MI) supervised learning algorithm for learning meaningful semantic actions, and the fuzzy backpropagation (FBP) learning algorithm for learning linguistic information. An experimental system is constructed to illustrate the performance and applicability of the proposed FLAN.
Robertson, TJ 1996, 'Embodied Actions in Time and Place: The Cooperative Design of a Multimedia, Educational Computer Game', Computer Supported Cooperative Work: the journal of collaborative computing, vol. 5, pp. 341-367.
Xu, G, Wang, N & Wang, J 1996, 'Determination of particle size distribution and mass concentration from the forward light flux scattered within two solid angles', Zhongguo Jiguang/Chinese Journal of Lasers, vol. 23, no. 5, pp. 432-436.
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Based on Fraunhofer diffraction theory a method was proposed to determine the size distribution as well as the absolute mass concentration of solid particles from the forward light flux scattering within two solid angles. The results of theoretical analysis and numerical simulation were given and the feasibility of this method and the prospect of its application were also indicated.
Antoniou, G & Williams, MA 1996, 'Default reasoning and belief revision in the CIN project', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 691-693.
Antoniou, G, Courtney, AP, Ernsttand, J & Williams, MA 1996, 'A system for computing constrained default logic extensions', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 237-250.
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© Springer-Verlag Berlin Heidelberg 1996. The aim of this paper is to describe the algorithmic foundations of the part of the program Exten responsible for the computation of extensions in Constrained Default Logic. Exten is a system that computes extensions for various default logics. The efficiency of the system is increased by pruning techniques for the search tree. We motivate and present these techniques, and demonstrate that they can cut down the size of the search tree significantly. Quite importantly, they complement well the recently developed stratification method. This technique has to be modified to work properly with Constrained Default Logic, and we show how this can be done. Exten supports experimentation with default logic, allowing the user to set various parameters. Also it has been designed to be open to future enhancements, which are supported by its object-oriented design. Exten is part of our long-term effort to develop an integrated toolkit for intelligent information management based on nonmonotonic reasoning and befief revision methods.
Bellé, G, Jay, CB & Moggi, E 1996, 'Functorial ML', PLILP ’96, Springer Verlag, pp. 32-46.
Jay, CB 1996, 'A fresh look at parametric polymorphism: covariant types', Nineteenth Australasian Computer Science Conference Proceedings, Melbourne, Australia, 31 January – 2 February, 1996, Australian Computer Science Communications, pp. 525-534.
Jay, CB 1996, 'Data categories', Computing: The Australasian Theory Symposium Proceedings, Melbourne, Australia, 29–30 January, 1996, Australian Computer Science Communications, pp. 21-28.
Jay, CB, Clarke, DG & Edwards, JJ 1996, 'Exploiting Shape in Parallel Programming', 1996 IEEE Second International Conference on Algorithms and Architectures for Parallel Processing: Proceedings, IEEE, pp. 295-302.
Jay, CB, Cole, M, Sekanina, M & Steckler, PA 1996, 'A Monadic Calculus for Parallel Costing in a Functional Language of Arrays'.
Leaney, J, Peterson, C & Drane, C 1996, 'Computer systems engineering in large groups', Proceedings - Frontiers in Education Conference, pp. 1491-1494.
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The subjects Computer Systems Analysis and Computer Systems Design within the computer systems engineering degree at UTS concern themselves with the specification, architecture, design and implementation of a computer based system of moderate complexity, covering electrical and mechanical hardware, computer hardware and software. Students are expected to develop the system to appropriate standards, using suitable techniques, within a defined process and operating within a team. The computer based system is concerned with the problem of the automatic assembly of (pseudo) chocolates into (pseudo) chocolate boxes. There are a variety of boxes and a variety of chocolates, which have to be assembled to (operator entered) orders. The class is divided into teams. A team comprises five groups. Each of four groups is responsible for one of the major subassemblies, and the fifth group is responsible for the systems engineering and telecommunications. The major subassemblies are the assembly robot, the box conveyor and (Vision) recognition system, the chocolate recognition system, and the supervisory control system. The project has been running for five years and this paper summarizes the history, reports on the development and analyses educational aspects. Student appreciation of the subjects has been entirely positive, with the most often made comment that finally they have understood why they have studied engineering for the previous four to five years.
Lin, CT & Juang, CF 1996, 'Adaptive neural fuzzy filter and its applications', IEEE International Conference on Fuzzy Systems, pp. 564-569.
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A new kind of nonlinear adaptive filter, the adaptive neural fuzzy filter (ANFF), based upon neural network's learning ability and fuzzy if-then rule structure is proposed in this paper. The ANFF is inherently a feedforward multilayered connectionist network which can learn by itself according to numerical training data or expert knowledge represented by fuzzy if-then rules. The adaptation here includes the construction of fuzzy if-then rules (structure learning), and the tuning of the free parameters of membership functions (parameter learning). There are no hidden nodes (i.e., no membership functions and fuzzy rules) initially, and both the structure learning and parameter learning are performed concurrently as the adaptation proceeds. Two major advantages of the ANFF are: 1) a priori knowledge can be incorporated into the ANFF which makes the fusion of numerical data and linguistic information in the filter possible; and 2) no predetermination, like the number of hidden nodes, must be given, since the ANFF can find its optimal structure and parameters automatically. To demonstrate the performance of the ANFF, two applications, the nonlinear channel equalization and the adaptive noise cancellation, are simulated.
Lin, CT, Juang, CF & Li, CP 1996, 'Temperature control with a neural fuzzy inference network', SOFT COMPUTING IN INTELLIGENT SYSTEMS AND INFORMATION PROCESSING, 1996 Asian Fuzzy Systems Symposium - Soft Computing in Intelligent Systems and Information Processing, I E E E, KENTING, TAIWAN, pp. 91-96.
Robertson, T.J. 1996, 'Participatory Design and Participative Practices in Small Companies', Proceedings of the Participatory Design Conference, Cambridge, Massachusetts, USA., pp. 35-43.
Rowe, D, Leaney, J & Lowe, D 1996, 'Development of a systems architecting process for computer based systems', Proceedings of the IEEE International Conference on Engineering of Complex Computer Systems, ICECCS, pp. 200-203.
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The need to address system architecture within the computer systems development process is well accepted. Despite this, the process of architecting is still not well understood. In this paper we discuss the need for an architecting process which goes beyond specific architecting methods in order to address issues such as architecture evaluation and tailoring of architectures to specific system requirements. Based on these needs we propose an architecting process which selects from existing methods, bases and performance indices to produce a verifiable architecture. This process provides a framework within which we can develop an architecture which has the maximum (measurable) likelihood of satisfying both functional and nonfunctional system requirements upon implementation.
Steeples, C, Unsworth, C, Bryson, M, Goodyear, P, Riding, P, Fowell, S, Levy, P & Duffy, C 1995, 'Technological support for teaching and learning: Computer-mediated communications in higher education (CMC in HE)', COMPUTERS & EDUCATION, Computer Assisted Learning Symposium (CAL 95), PERGAMON-ELSEVIER SCIENCE LTD, UNIV CAMBRIDGE, CAMBRIDGE, ENGLAND, pp. 71-80.
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Wang, NN & Xu, GD 1996, 'Optical on-line measurements of particulate mass concentration and particle size pollutant emission', Proceedings of the International Conference on Energy and Environment, ICEE, pp. 777-781.
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In this paper, an optical measuring technique which can be used in in-situ measurements and gives the full particle size information as well as the absolute mass concentration of pollutant emissions is presented, according to the light scattering theory. Based on the Fraunhofer diffraction theory, the information about the particle size can be obtained by measuring the forward light flux at two small solid angles, and the mass concentration from the extinction measurement. Theoretical calculations and computer simulation are made to verify the validity and applicability of the presented method. Results show that it can be used to monitor and supervise the pollutant emissions in different industrial processes.
Zowghi, D 1996, 'A Framework for Reasoning about Requirements Evolution', Proceedings of the 4th Pacific Rim International Conference on Artificial Intelligence (PRICAI'96), Pacific Rim International Conference on Artificial Intelligence, Cairns, Australia, pp. 157-168.
Zowghi, D & Offen, R 1996, 'A Logical Framework for Modeling and Reasoning about the Evolution of Requirements', Proceedings of the Third IEEE International Symposium on Requirements Engineering, IEEE International Symposium on Requirements Engineering, pp. 247-257.