S. Goonatilake and S. Khebbal, editors. Intelligent Hybrid Systems. John Wiley & Sons, Chichester, 1995.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....so far devoted to control applications. Moreover, as the development of fuzzy logic controllers gets more grounded, other related areas are evolving quickly. This is the case of fuzzy modelling which, by the way of hybrid approaches based on fuzzy logic, neural networks and genetic algorithms [3], have produced important contributions. Hybrid approaches now allow the development of models whose quality is excellent [7] In spite of this relative importance, they tend to be difficult to apply to practical situations because of memory and computation time they require. In this paper it is ....

S. Goonatilake, S. Khebbal, Intelligent Hybrid Systems, John Wiley & Sons, 1995.

....trade balance [11] are two fundamental factors influencing the long term movements of exchange rates. For short term FX prediction, however, the forecasting methods used so far, be they technical analysis [25] statistics or neural nets [12,17] base their predictions on quantifiable information [2,5,6,9,10,13,14,23,24]. As input they usually take huge amounts of quoted exchange rates between various currencies. The innovation of our approach is that we make use of non numeric and hard to quantify data derived from textual information. In contrast to time series data [32] containing the effect only (e.g. the ....

S. Goonatilake and S. Khebbal, Intelligent Hybrid Systems, Wiley, 1995.

.... or highly structured (as opposed to homogeneous fully connected) ANN s are just two examples of a wide range of approaches to designing intelligent systems [99] 34] 35] Of particular interest are alternative designs (including synergistic hybrids of ANN and AI designs) for intelligent systems [20], 28] 32] 34] 35] 46] 92] 95] 99] Examples of such systems include: neural architectures for database query processing [10] generation of context free languages [100] rule based inference [12] 72] 88] 94] computer vision [4] 58] natural language processing [6] 13] ....

....extensive table lookup. A more general goal of this paper is to explore the design of massively parallel architectures for symbol processing using neural associative memories (processors) as key components. This paper takes a small step in this direction and adds to the growing body of literature [20], 34] 46] 95] that demonstrates the potential benefits of integrated neuralsymbolic architectures that overcome some of the limitations of today s ANN and AI systems. APPENDIX This Appendix illustrates how the modules of an NNStack together realize a stack by considering several successive ....

S. Goonatilake and S. Khebbal, Eds., Intelligent hybrid systems. London, U.K.: Wiley, 1995.

....of the main methods used, outline the work that is presented here, and provide additional references. We will also highlight some important general issues and trends. 1 Introduction In recent years, the research area of hybrid and neural processing has seen a remarkably active development [62, 50, 21, 4, 48, 87, 75, 76, 25, 49, 94, 13, 74, 91]. Furthermore, there has been an enormous increase in the successful use of hybrid intelligent systems in many diverse areas such as speech natural language understanding, robotics, medical diagnosis, fault diagnosis of industrial equipment and financial applications. Looking at this research ....

S. Goonatilake and S. Khebbal. Intelligent Hybrid Systems. Wiley, Chichester, 1995.

....of the main methods used, outline the work that is presented here, and provide additional references. We will also highlight some important general issues and trends. 1 Introduction In recent years, the research area of hybrid and neural processing has seen a remarkably active development [62, 50, 21, 4, 48, 87, 75, 76, 25, 49, 94, 13, 74, 91]. Furthermore, there has been an enormous increase in the successful use of hybrid intelligent systems in many diverse areas such as speech natural language understanding, robotics, medical diagnosis, fault diagnosis of industrial equipment and nancial applications. Looking at this research area, ....

S. Goonatilake and S. Khebbal. Intelligent Hybrid Systems. Wiley, Chichester, 1995.

....and computational overheads but empower neural systems with symbolic processing abilities and vice versa. Previous work reviewing the field of hybrid systems has been somewhat unconstrained since most of the review effort has attempted to encompass very different hybrid intelligent techniques [30, 40] e.g. neural networks, rule based systems, genetic algorithms and neuro fuzzy logic. In order to give detailed technical descriptions of hybrid systems we focus upon those hybrid systems using the two most commonly used elements, namely rule based components and neural networks. The next section ....

....of the human mind originate with the low level, fine grained processing carried out by biological neurons. A number of such systems have been developed [75, 76, 84] The term unified is fairly descriptive and has been retained within our classification scheme. Goonatilake and Khebbal [30] have defined their scheme on the basis of functionality, architecture and communication requirements. This scheme is more general than Medsker s scheme and can therefore be applied to systems with components other than neural and symbolic elements. The authors have based their scheme upon the ....

S. Goonatilake and S.Khebbal. Intelligent Hybrid Systems, John Wiley and Sons, Chichester, 1995.

....namely the incapacity of ANN to explain their behaviour and on the other hand, the acquisition of knowledge for AI systems, are important problems to be adressed. Recently, there has been an increased interest in hybrid systems that integrate AI technologies and ANN to solve this kind of problems [2]. It its worth to remark here that essentially hybrid systems have been developed that entail several modules, each implemented in a different technology, and that cooperate with another. In contrast, we are mainly interested in hybrid systems that perform a knowledge conversion, i.e. a transition ....

....one sequence, the temporal pattern also just has one sequence. Otherwise, the temporal pattern would be described by an alternation of sequences using an or . 3. Conclusion Recently, different kinds of hybrid systems that integrate AI technologies and neural networks have been developed [2]. We emphasize that above all cooperative hybrid systems have been developed, i.e. a cooperation between several modules Event 3 Event 1 Event implemented in different technologies exists. The main difference to our approach is that in cooperative hybrid system no transition between different ....

Goonatilake, S., Khebal, S. (Eds.): Intelligent Hybrid Systems, Wiley & Sons, New York, 1995.

....been referred to as Hybrid Systems, High Level Connectionism, Symbolic Connectionism, Symbol Processing Connectionist Systems and the like. Though work in this area has been on going since the resurgence of Connectionism in the late eighties [7] 8] a proliferation of new books on the subject ([9] [10] 11] 12] for example) indicates its increasing importance. To date, the field of Hybrid Systems has produced some interesting applications [9] 10] 11] 12] 13] and some ground work has been laid concerning the proper integration of various approaches to modeling cognition; however, the ....

....work in this area has been on going since the resurgence of Connectionism in the late eighties [7] 8] a proliferation of new books on the subject ( 9] 10] 11] 12] for example) indicates its increasing importance. To date, the field of Hybrid Systems has produced some interesting applications [9] [10] 11] 12] 13] and some ground work has been laid concerning the proper integration of various approaches to modeling cognition; however, the critical next step must be the development of a formal theory for the integration of Connectionism with Symbolism and thus for a general theory of ....

Goonatilake, Suran and Khebbal, Sukhdev (eds.), Intelligent Hybrid Systems, John Wiley & Sons, 1995.

....problem. 3. Hybrid Algorithm There is currently considerable confusion as to exactly what a hybrid system is. Much of the problem lies in the different interpretation of functionality and architecture of these systems. As shown in Figure 6, we can classify hybrid systems in the following manner [10]: ffl Intercommunicating hybrids are independent, self contained, processing modules that exchange information, and perform separate functions to generate solutions. Communication and synchronization is usually performed by the aid of a controller. When a problem can be subdivided into distinct ....

S. Goonatilake and S. Khebbal, Intelligent Hybrid Systems, To Appear In: Proceedings of the First Singapore International Conference On Intelligent Systems, 1992.

....so far devoted to control applications. Moreover, as the development of fuzzy logic controllers gets more grounded, other related areas are evolving quickly. This is the case of fuzzy modelling which, by the way of hybrid approaches based on fuzzy logic, neural networks and genetic algorithms [3], have produced important contributions. Hybrid approaches now allow the development of models whose quality is excellent [7] In spite of this relative importance, they tend to be difficult to apply to practical situations because of memory and computation time they require. In this paper it is ....

S. Goonatilake, S. Khebbal, Intelligent Hybrid Systems, John Wiley & Sons, 1995.

.... classification, signal processing, and control, their use in complex symbolic computing tasks (including storage and retrieval of records in large databases, and inference in deductive knowledge bases) is only beginning to be explored (Honavar, Uhr, 1994; Sun, 1994; Levine Aparicioiv, 1994; Goonatilake Khebbal, 1995). This paper explores the application of neural networks to database query processing. Database query can be viewed as a process of table lookup which is used in a wide variety of computing applications. Examples of such lookup tables include: routing tables used in routing of messages in ....

Goonatilake, S. and Khebbal, S. (Ed.) Intelligent Hybrid Systems. Wiley, London, 1995.

....Fuzzy Logic, and Symbolic Artificial Intelligence. Approaches from these different fields each have their strengths and their weaknesses. Recent research suggests that further progression may depend upon the synergistic combination of several complementary approaches from these different fields [4][6] 11] This new synergistic approach to machine intelligence is sometimes referred to as Hybrid Systems. One such possibility is the combination of Evolutionary Computation (EC) 3] 10] with Neural Networks (NN) 9] 13] For some examples of such combinations, the reader is referred to ....

Goonatilake, Suran and Khebbal, Sukhdev (eds.), Intelligent Hybrid Systems, John Wiley & Sons, 1995.

....language processing has a lot of potential to overcome the gap between a neural level and a symbolic conceptual level. ii 1 Motivation for hybrid symbolic connectionist processing In recent years, the field of hybrid symbolic connectionist processing has seen a remarkable development [48, 38, 18, 2, 36, 59, 55, 20, 37, 61, 9]. Currently it is still an open issue whether connectionist or symbolic approaches alone will be sufficient to provide a general framework for processing natural language [51, 11, 27, 56] However, since human language capabilities are based on real neural networks in the brain, artificial neural ....

S. Goonatilake and S. Khebbal. Intelligent Hybrid Systems. Wiley, Chichester, 1995.

....of the user model. A 60 L. Ambrosini et al. problem we have noticed is that this type of classification must be made in the light of incomplete and often conflicting information. Our proposed solution (see also Micarelli and Sciarrone, 1996) consists in the use of a function replacing hybrid (Goonatilake and Khebbal, 1995), where an artificial neural network implements (i.e. is functionally equivalent to) the indexing module. The procedural attachment is activated according to the selected stereotype and the actual pattern. The old cases are used as training records for the network. As a result, the metric of the ....

Goonatilake, S., and Khebbal, S., eds. (1995). Intelligent Hybrid Systems. Wiley.

....R.Lowen and M.Roubens eds. Fuzzy Logic: State of the Art, Theory and Decision Library, Series D (Kluwer Academic Publisher, Dordrecht, 1993) 193200. 56] R. Fuller and H. J. Zimmermann, Fuzzy reasoning for solving fuzzy mathematical programming problems, Fuzzy Sets and Systems 60(1993) 121 133. [57] R. Fuller and E. Triesch, A note on law of large numbers for fuzzy variables, Fuzzy Sets and Systems, 55(1993) 58] M.M. Gupta and D.H. Rao, On the principles of fuzzy neural networks, Fuzzy Sets and Systems, 61(1994) 1 18. 59] H. Hamacher, H.Leberling and H. J.Zimmermann, Sensitivity ....

S. Goonatilake and S. Khebbal eds., Intelligent Hybrid Systems, John Wiley and Sons, New York 1995.

....are objects, just like the normal 3D primitives: they have 3D presence and can interact with other 3D objects. A natural consequence of this design is easy hybridisability of techniques, widely considered as vital to the success of intelligent techniques in solving realistically complex problems [GoKh95]. This infrastructure of primitive forms, intelligent techniques and high level language makes it possible to build applications to deal with a broad range of problems, from the generation of architectural form, spatial optimisation, object recognition and clustering, and inducing rules and ....

Goonatilake S., and Khebbal S (eds), Intelligent Hybrid Systems. John Wiley (1995).

....of ANN to a broad range of tasks in pattern classification, control, function approximation, and system identification, their use in symbolic computing tasks (e.g. storage and retrieval of records in large databases and knowledge bases, language processing, etc. is only beginning to be explored [Goonatilake and Khebbal, 1995; Honavar, 1994; Honavar and Uhr, 1994, 1995; Levine and Apariciov, 1994; Sun and Bookman, 1995; Uhr and Honavar, 1994] The capabilities of neural network models (in particular, recurrent networks of threshold logic units or McCulloch Pitts neurons) in processing and generating sequences (strings ....

Goonatilake S. and Khebbal S. (Ed.), Intelligent Hybrid Systems. Wiley, London, 1995.

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S. Goonatilake and S. Khebbal, editors. Intelligent Hybrid Systems. John Wiley & Sons, Chichester, 1995.

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S. Goonatilake and S. Khebbal, editors. Intelligent Hybrid Systems. John Wiley & Sons, Chichester, 1995.

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Goonatilake, S., and Khebbal, S. Intelligent Hybrid Systems. Wiley and Sons, 1995.

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Goonatilake, S. and Khebbal, S., editors (1995). Intelligent Hybrid Systems. John Wiley & Sons.

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S. Goonatilake and S. Khebbal, editors. Intelligent Hybrid Systems. John Wiley & Sons, Chichester, 1995.

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Goonatilake, S. and Khebbal, S. (1995). Intelligent Hybrid Systems. John Wiley & Sons.

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Goonatilake, S. and Khebbal, S. (Ed.), Intelligent Hybrid Systems. Wiley, London, 1995.

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