Dyson, LE 1994, 'Schizophrenia as a poetic model in andrea zanzotto', Forum Italicum, vol. 28, no. 2, pp. 342-357.
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HSIAO, CH, LIN, CT & CASSIDY, M 1994, 'APPLICATION OF FUZZY-LOGIC AND NEURAL NETWORKS TO AUTOMATICALLY DETECT FREEWAY TRAFFIC INCIDENTS', JOURNAL OF TRANSPORTATION ENGINEERING-ASCE, vol. 120, no. 5, pp. 753-772.
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LIN, CT & LEE, CSG 1994, 'REINFORCEMENT STRUCTURE PARAMETER LEARNING FOR NEURAL-NETWORK-BASED FUZZY-LOGIC CONTROL-SYSTEMS', IEEE TRANSACTIONS ON FUZZY SYSTEMS, vol. 2, no. 1, pp. 46-63.
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Xuecheng, L & Guangquan, Z 1994, 'Lattice-valued fuzzy measure and lattice-valued fuzzy integral', Fuzzy Sets and Systems, vol. 62, no. 3, pp. 319-332.
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In this paper, (1) the concepts of lattice-valued fuzzy measure (with no valuation property) and lower (resp. upper) lattice-valued fuzzy integral are proposed, which give the unified discription to the fuzzy measures and fuzzy integrals studied by Delgado and Moral, Qiao, Ralescu, Adams, Sugeno, Wang, and Zhang; (2) some asymptotic structural characteristics of lattice-valued fuzzy measures are introduced, and some relations between them are given; (3) some concepts of convergences for lattice-valued functions are defined, and Riesz' theorem, Egoroff's theorems and Lebesgue's theorem for lattice-valued measurable function are proved; (4) the monotone increasing (resp. decreasing) convergence theorem and almost (resp. pseudo almost) everywhere convergence theorrem for lower (resp. upper) lattice-valued fuzzy integral are shown under some weak conditions. © 1994.
Jay, CB 1994, 'A survey of shapely types', Proceedings of Computing: the Australian Theory Seminar, University of Technology, Sydney, 17-19 December, 1994, University of Technology, Sydney, pp. 81-88.
Jay, CB 1994, 'Matrices, monads and the fast Fourier transform', Proceedings of the Massey Functional Programming Workshop 1994, pp. 71-80.
Jay, CB & Cockett, JRB 1994, 'Shapely Types and Shape Polymorphism', Programming Languages and Systems – ESOP ’94: 5th European Symposium on Programming, Edinburgh, U.K., April 1994, Proceedings, Springer Verlag, pp. 302-316.
Lin, CJ & Lin, CT 1994, 'ART-based fuzzy adaptive learning control network', pp. 357-362.
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This paper addresses the structure and associated on-line learning algorithms of a feedforward multilayered connectionist network for realizing the basic elements and functions of a traditional fuzzy logic controller. The proposed Fuzzy Adaptive Learning Control Network (FALCON) can be contrasted with the traditional fuzzy logic control systems in their network structure and learning ability. An on-line structure/parameter learning algorithm, called FALCON-ART, is proposed for constructing the FALCON dynamically. The FALCON-ART can partition the input/output space in a flexible way based on the distribution of the training data. Hence it can avoid the problem of combinatorial growing of partitioned grids in some complex systems. It combines the backpropagation learning scheme for parameter learning and the fuzzy ART algorithm for structure learning. More notably, the FALCON-ART can on-line partition the input/output spaces, tune membership functions and find proper fuzzy logic rules dynamically without any a priori knowledge or even any initial information on these.
Lin, CJ & Lin, CT 1994, 'ART-based fuzzy adaptive learning control network', IEEE International Conference on Fuzzy Systems, pp. 1-5.
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This paper addresses the structure and associated on-line learning algorithms of a feedforward multilayered connectionist network for realizing the basic elements and functions of a traditional fuzzy logic controller. The proposed Fuzzy Adaptive Learning COntrol Network (FALCON) can be contrasted with the traditional fuzzy logic control systems in their network structure and learning ability. An on-line structure/parameter learning algorithm, called FALCON-ART, is proposed for constructing the FALCON dynamically. The FALCON-ART can partition the input/output space in a flexible way based on the distribution of the training data. Hence it can avoid the problem of combinatorial growing of partitioned grids in some complex systems. It combines the backpropagation learning scheme for parameter learning and the fuzzy ART algorithm for structure learning. More notably, the FALCON-ART can on-line partition the input/output spaces, tune membership functions and find proper fuzzy logic rules dynamically without any a priori knowledge or even any initial information on these.
Lin, CT 1994, 'FALCON: A fuzzy adaptive learning control network', pp. 228-232.
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This paper proposes a Reinforcement Fuzzy Adaptive Learning COntrol Network (RFALCON) for solving various reinforcement learning problems. The proposed RFALCON is constructed by integrating two Fuzzy Adaptive Learning COntrol networks (FALCON), each of which is a connectionist model with a feedforward multilayered network developed for the realization of a fuzzy logic controller. An on-line structure/parameter learning algorithm, called RFALCON-ART, is proposed for constructing the RFALCON dynamically. The proposed R-FALCON also preserves the advantages of the original FALCON, such as the ability to do on-line partition the input/output spaces, tune membership functions, and find proper fuzzy logic rules. In its initial form, there is no membership function, fuzzy partition, and fuzzy logic rule. They are created and begin to grow as the first reinforcement signal arrives. The users thus need not give it any a priori knowledge or even any initial information on these.
Robertson, T.J. 1994, '"We Can Do It Better": Communication and the Control of Work Practices', Proceedings of OZCHI, Computer Human Interaction Special Interst Group of the Ergonomics Society of Australia, Melbourne, pp. 295-300.
Stone, JV & Lister, R 1994, 'On the relative time complexities of standard and conjugate gradient back propagation', IEEE International Conference on Neural Networks - Conference Proceedings, pp. 84-87.
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It is well known that conjugate gradient back propagation is much faster than standard back propagation on many given problem instances. However, it has not been established that the conjugate gradient form has a lower time complexity than the standard algorithm. We describe an empirical estimation of the algorithms' respective time complexities, on N2N encoder problems. The algorithms were found to have near equal median time complexities, approximately O(N4).
Williams, MA 1994, 'On the logic of theory base change', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 86-105.
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© 1994, Springer Verlag. All rights reserved. Recently there has been considerable interest in change operators for theory bases, rather than entire theories. Especially since such operators could support computer-based implementations of revision systems. However, a perceived problem associated with theory base change operators is their sensitivity to the syntax of the theory base used. Although it has been argued that this sensitivity should reflect a higher level of commitment to formulae in the theory base than formulae derivable from the theory base. In this paper we develop a logic of theory base change, using constructions based on ensconcements. We show whenever two theory bases have equivalent ensconcements, the logical closure of their theory base revisions are identical. Moreover, we give explicit relationships associating theory base revision and theory base contraction, and provide explicit relationships between theory base change and theory change operations. We claim that these relationships show that our theory base revision and theory base contraction operators exhibit desirable behaviour.
Jay, CB & Cockett, JRB 1994, Shapely Types and Shape Polymorphism: Extended Version, no. UTS-SOCS-94-??, University of Technology, Sydney.