Catchpoole, DR & Stewart, BW 1994, 'Inhibition of topoisomerase II by aurintricarboxylic acid: implications for mechanisms of apoptosis.', Anticancer Res, vol. 14, no. 3A, pp. 853-856.
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Internucleosomal fragmentation of DNA, the most widely used biochemical indicator of apoptosis, is believed to contribute to loss of viability because the nuclease inhibitor, aurintricarboxylic acid, delays or prevents cell death in a range of experimental systems. We report here that auritricarboxylic acid inhibits topoisomerase II in vitro, the concentration required (< or = 0.2 microM) being less than that usually employed in studies of apoptosis. Since topoisomerase II mediates chromatin condensation during apoptosis, the efficacy of ATA in preventing or delaying cell death may not be the result of nuclease inhibition.
Chin-Teng Lin & 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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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, C, Lin, C & Cassidy, M 1994, 'Application of Fuzzy Logic and Neural Networks to Automatically Detect Freeway Traffic Incidents', Journal of Transportation Engineering, vol. 120, no. 5, pp. 753-772.
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Voinov, A, Cibuzar, A & Nawrocki, T 1994, 'Sustainable Development on a Watershed Scale Russian Case Study—Pronya River', Lake and Reservoir Management, vol. 9, no. 1, pp. 46-50.
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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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YING, MS 1994, 'A LOGIC FOR APPROXIMATE REASONING', JOURNAL OF SYMBOLIC LOGIC, vol. 59, no. 3, pp. 830-837.
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YING, MS 1994, 'ON THE METHOD OF NEIGHBORHOOD-SYSTEMS IN FUZZY TOPOLOGY', FUZZY SETS AND SYSTEMS, vol. 68, no. 2, pp. 227-238.
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In this paper, we revivify the theory of neighborhood systems in fuzzy topology with the method used to develop fuzzifying topology and find another kind of reasonable neighborhood structure in fuzzy topology except quasi-neighborhood systems of Pu and L
Cheng-Jian Lin & Chin-Teng Lin 1970, 'An ART-based fuzzy adaptive learning control network', NAFIPS/IFIS/NASA '94. Proceedings of the First International Joint Conference of The North American Fuzzy Information Processing Society Biannual Conference. The Industrial Fuzzy Control and Intelligent Systems Conference, and the NASA Joint Technology Wo, NAFIPS/IFIS/NASA '94. First International Joint Conference of The North American Fuzzy Information Processing Society Biannual Conference. The Industrial Fuzzy Control and Intelligent Systems Conference, and the NASA Joint Technology Workshop on Neural Networks and Fuzzy Logic, IEEE, 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.
Chin-Teng Lin 1970, 'FALCON: a fuzzy adaptive learning control network', NAFIPS/IFIS/NASA '94. Proceedings of the First International Joint Conference of The North American Fuzzy Information Processing Society Biannual Conference. The Industrial Fuzzy Control and Intelligent Systems Conference, and the NASA Joint Technology Wo, NAFIPS/IFIS/NASA '94. First International Joint Conference of The North American Fuzzy Information Processing Society Biannual Conference. The Industrial Fuzzy Control and Intelligent Systems Conference, and the NASA Joint Technology Workshop on Neural Networks and Fuzzy Logic, IEEE, 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.
Lin, CJ & Lin, CT 1970, '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.
Stone, JV & Lister, R 1970, '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).
