Skip to main content
SpringerLink
Log in

Intelligent Fusion and Transformation Systems

  • Chapter
Engineering Intelligent Hybrid Multi-Agent Systems

  • 101 Accesses

Abstract

In recent times, research efforts have been directed towards hybridization of various intelligent methodologies in order to solve complex industrial problems. At the same time these research efforts have been undertaken to develop a better understanding of the human information processing system. Today, one can find a number of applications involving hybridization of intelligent methodologies like knowledge based systems, fuzzy systems, genetic algorithms, and case based reasoning with artificial neural networks. The central methodology of many hybrid systems has been artificial neural networks. In fact, 2/3rd of the applications involving intelligent hybrid systems use neural networks. Broadly, in these applications neural networks are either used as the primary problem solving entity, or are used in conjunction with other intelligent methodology/ies which have a distinct and separate role to play in the problem solving process. The former are categorized as fusion and/or transformation based approaches, and the later are categorized as combination approaches. However, fusion and/or transformation based approaches center around not only neural networks but also center around the other other methodology, namely, genetic algorithms. Similarly, the combination approaches can involve intelligent methodologies other than neural networks also. The goal of this chapter is to provide an overview to the reader about fusion and transformation based synergies with neural network and genetic algorithms as the primary problem solving entities. In this direction, this chapter looks at the neuro-symbolic systems, neuro-fuzzy systems, genetic-neuro systems, and genetic-fuzzy systems.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • Ajjanagadde, V. and Shastri, L. (1991), “Rules and Variables in Neural nets,” Neural Computation, vol. 3, pp. 121–134.

    Article  Google Scholar 

  • Bezdek, J.C. (1981), Pattern Recognition with Fuzzy Objective Function Algorithms, Advanced Applications in Pattern Recognition, Plenum Press.

    Google Scholar 

  • Bezdek, J.C., Tsao, E.C., and Pal, N.R. (1992), “Fuzzy Kohonen Clustering Networks,” IEEE International Conference on Fuzzy Systems, San Diego, pp. 1035–1043.

    Google Scholar 

  • Bezdek, J.C. (1993), “A Review of Probabilistic, Fuzzy, and Neural Models for Pattern Recognition,” Journal of Intelligent and Fuzzy Systems, vol. 1 no. 1, pp. 1–25.

    MathSciNet  Google Scholar 

  • Bulsari, A., Saxen, H., and Kraslawski, A. (1992), “Fuzzy Simulation by an Artificial Neural Network,” Engineering Applications of Artificial Intelligence, vol. 4, no. 5, pp. 404–406.

    Google Scholar 

  • Carpenter, G. A., Grossberg, S., Markuzon, N., Reynolds, J.H., and Rosen, D. B. (1992), “Fuzzy ARTMAP: A Neural Network Architecture for Incremental Supervised Learning of Analog Multidimensional Maps,” IEEE Transactions on Neural Networks, vol. 3, no. 5, pp. 698–713.

    Article  Google Scholar 

  • Carpenter, G. A., and Gjaja, M. N. (1994), “Fuzzy ART Choice Functions,” Proceedings of the World Congress on Neural Networks, vol. 1, no. 5, pp 713–722

    Google Scholar 

  • Chandrasekaran, B., “What kind of Information Processing is Intelligence,” Foundations of Artificial Intelligence, Cambridge University Press, pp14–45, 1990.

    Google Scholar 

  • Edelman, G. (1992), Bright Air, Brilliant Fire: On the Matter of the Mind, Raven Press, New York, USA.

    Google Scholar 

  • Fahlman, S. E. and Hinton, G. E. (1987), “Connectionist Architectures for Artificial Intelligence,” IEEE Computer, January, pp. 100–109.

    Google Scholar 

  • Fu, L. M., and Fu, L. C. (1990), “Mapping Rule-Based Systems into Neural Architecture,” Knowledge-Based Systems, vol. 3, no 1, pp.48–56

    Article  Google Scholar 

  • Fukuda, T., Ishigami, H. et. al. (1993), “Structure Optimization of Fuzzy Gaussian Neural Network using Genetic Algorithm,” Proceedings of Intelligent Computer Aided Manufacturing.

    Google Scholar 

  • Gallant, S. (1988), “Connectionist Expert Systems,” Communications of the ACM, February pp. 152–169.

    Google Scholar 

  • Hayashi, Y., Czogala, E., and Buckley, J.J. (1992), “Fuzzy Neural Controller,” Proceedings of 1992 IEEE International Conference on Fuzzy Systems, San Diego, USA, pp. 197–202.

    Google Scholar 

  • Hinton, G. E. (1990), “Mapping Part-whole Hierarchies into Connectionist Networks,” Artificial Intelligence, vol. 46, no. 1–2, pp. 47–76.

    Article  Google Scholar 

  • Hudson, D. L., Cohen, M. E. and Anderson, M. F. (1991), “Use of Neural Network Techniques in a Medical Expert System,” International Journal of Intelligent Systems, Vol. 6, pp213–223.

    Article  Google Scholar 

  • Hung, C.C. (1993), “Building a Neuro-Fuzzy Learning Control System,” AI Expert, vol. 8, no. 11, pp. 40–49.

    Google Scholar 

  • Huntsberger, T.L., and Ajjimarangsee, P. (1990), “Parallel Self-Organising Feature Maps for Unsupervised Pattern Recognition,” International Journal of General Systems, vol. 16, no.4, pp. 357–372.

    Article  Google Scholar 

  • Ishibuchi, H., Tanaka, H., and Okada, H. (1994), “Interpolation of Fuzzy if-then Rules by Neural Networks,” International Journal of Approximate Reasoning, January, vol. 10, no. 1, pp. 3–27.

    Article  MATH  Google Scholar 

  • Ishibuchi, H., Nozaki, K., Yamamoto, N., and Tanaka, H. (1993), “Genetic Operations for Rule Selection in Fuzzy Classification Systems,” Fifth IFSA World Congress, pp. 769–772.

    Google Scholar 

  • Lacher, R. C. (1992), “Back-Propagation Learning in Expert Networks,” IEEE Transactions on Neural Networks, vol. 3, no 1, pp. 62–71.

    Article  Google Scholar 

  • Lee, C.C. (1990), “Fuzzy Logic in Control Systems: Fuzzy Logic Controller-Part I-II,” IEEE Transactions on Systems, Man and Cybernetics, vol. 20, no 2, pp. 404–435.

    Article  MATH  Google Scholar 

  • Lee, M., Lee, S., and Park, C.H. (1994), “A New Neuro-Fuzzy Identification Model of Nonlinear Systems,” International Journal of Approximate Reasoning, January, vol. 10, no. 1, pp. 29–44.

    Article  MATH  Google Scholar 

  • Karr, C., Freeman, L., and Meredith, D. (1989), “Improved Fuzzy Process Control of Spacecraft Autonomous Rendezvous using a Genetic Algorithm,” Proc. of the SPIE Conference on Intelligent Control Adaptive Systems, pp. 274–283.

    Google Scholar 

  • Karr, C. (1992), “Design of an Adaptive Fuzzy Logic,” Proc. of International Conference on Genetic Algorithms, pp. 450–457.

    Google Scholar 

  • Kasabov, N. K. (1994), “Hybrid Connectionist Fuzzy Systems for Speech Recognition and the use of Connectionist Production Systems,” Advances in Fuzzy Logic, Neural networks and Genetic Algorithms, pp. 19–33.

    Google Scholar 

  • Kasabov, N. K. (1995), “Hybrid Fuzzy Connectionist Rule-based Systems and the Role of Fuzz Rules Extraction,” 1995 IEEE International Conference on Fuzzy Systems, vol. 1, pp. 49–56.

    Google Scholar 

  • Kokinov, B. (1995), “Micro-Level Hybridization in the Cognitive Architecture DUAL,” Working Notes on Connectionist-Symbolic Integration: From Unified to Hybrid Approaches, IJCAI95, Montreal, Canada, pp. 37–43.

    Google Scholar 

  • Keller, J.M., and Hunt, D.J. (1985), “Incorporating Fuzzy Membership Functions into the Perceptron Algorithms,” IEEE Transactions Pattern Analysis and Machine Intelligence, PAMII, pp. 693–699.

    Google Scholar 

  • Keller, J.M., and Tahani, H. (1992a),“Implementation of Conjunctive and Disjunctive Fuzzy Logic Rules with Neural Networks,” Fuzzy Models for Pattern Recognition, edited by Bezdek and Pal, IEEE Press, pp. 513–528.

    Google Scholar 

  • Keller, J. M., Krishnapuram, R., and Rhee, F.C. (1992b), “Evidence Aggregation networks for Fuzzy Logic Inference,” IEEE Transactions on Neural Networks, September, vol. 2, No.5, pp. 769–769.

    Google Scholar 

  • Kohonen, T. (1990), Self Organization and Associative Memory, Springer-Verlag.

    Google Scholar 

  • Kwan, H.K., and Cai, Y. (1994), “A Fuzzy Neural Network and its Application to Pattern Recognition,” IEEE Transactions on Fuzzy Systems, vol. 2, no 3, pp. 185–193.

    Article  Google Scholar 

  • Lallement, Y., and Alexandre, F. (1995), “Cognitive Aspects of Neurosym-bolic Integration,” Working Notes of IJCAI95 Workshop on Connectionist-Symbolic Integration: From Unified to Hybrid Approaches, Montreal, Canada, pp. 7–11.

    Google Scholar 

  • Maeda, A., Ichimori, T., and Funabashi, M. (1993), “FLIP-net: A Network Representation of Fuzzy Inference Procedure and its Application to Fuzzy Rule Structure Analysis,” Proceedings of Second IEEE International Conference on Fuzzy Systems, pp. 391–395.

    Google Scholar 

  • Nauck, D., and Kruse, R. (1993), “A Fuzzy Neural Network Learning Fuzzy Control Rules and Membership Functions by Fuzzy Error Backpropagation,” Proceedings of the IEEE International Conference on Neural Networks, San Francisco, vol. 2, pp. 1022–1027.

    Google Scholar 

  • Nobre, F. S. M. (1995), “Genetic-Neuro-Fuzzy Systems: A Promising Fusion,” 1995 IEEE International Conference on Fuzzy Systems, vol. 1, pp. 259–266.

    Google Scholar 

  • Pal, S. K., Mitra, A. (1992), “Multilayer Perceptron, Fuzzy Sets, and Classification,” IEEE Transactions on Neural Networks, vol. 3, no. 5, pp. 683–697

    Article  Google Scholar 

  • Pedrycz, W. (1991), “Neurocomputations in Relational Systems,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 13 no 3, pp. 289–297.

    Article  Google Scholar 

  • Park, N. S., and Robertson, D. (1995), “A Localist Network Architecture for Logical Inference Based on Temporal Synchrony Approach to Dynamic Variable Binding,” Working Notes on Connectionist-Symbolic Integration: From Unified to Hybrid Approaches, IJCAI95, Montreal, Canada, pp. 63–68.

    Google Scholar 

  • Pedrycz, W. (1992), “Fuzzy Neural Networks with Reference Neurons as Pattern Classifiers,” IEEE Transactions on Neural Networks, vol. 3 no 5, pp.770–775.

    Article  Google Scholar 

  • Pedrycz, W., and Rocha, A. (1993), “Fuzzy-Set based Models of Neurons and Knowledge based Neurons,” IEEE Transactions on Fuzzy Systems, vol. 1 no 4, pp.254–266.

    Article  Google Scholar 

  • Plate, T. A. (1995), “A Distributed Representation for Nested Compositional Structure,” Working Notes on Connectionist-Symbolic Integration: From Unified to Hybrid Approaches, IJCAI95, Montreal, Canada, pp. 69–74.

    Google Scholar 

  • Romaniuk, S. (1994), “Applying Crossover Operators to Automatic Neural Network Construction,” Proceedings of the First IEEE Conference on Evolutionary Computation, vol. 2 pp. 750–753.

    Google Scholar 

  • Saito, K. and Nakano, R. (1988), “Medical Diagnostic Expert System Based on PDP Model,” Proceedings of the IEEE International Conference on Neural Networks, San Diego, USA.

    Google Scholar 

  • Sestito, S. and Dillon, T. (1991), “The use of Sub-Symbolic Methods for the Automation of Knowledge Acquisition for Expert Systems,” Eleventh International Conference on Expert Systems and their Applications, France, pp. 317–328.

    Google Scholar 

  • Sestito, S. and Dillon, T. (1994), Automated Knowledge Acquisition, Prentice Hall, Sydney, Australia.

    MATH  Google Scholar 

  • Sethi, I.K. (1990), “Entropy Nets: From Decision Trees to Neural Networks,” Proceedings of the IEEE, vol. 78, no 10, pp. 1605–1613.

    Article  Google Scholar 

  • Sethi, I.K. (1991), “Decision Tree Performance Enhancement using an Artificial Neural Network Implementation,” Artificial Neural Networks and Statistical Pattern Recognition: Old and New Connections, eds I.K. Sethi & A.K. Jain, Elssevier Science Publishers, Amsterdam, pp. 71–86.

    Google Scholar 

  • Shastri, L. (1988), “A Connectionist Approach to Knowledge Representation and Limited Inference,” Cognitive Science, vol. 12, pp. 331–392.

    Article  Google Scholar 

  • Shastri, L. and Ajjanagadde, V. (1989), “A Connectionist System for Rule Based Reasoning with Multi-Place Predicates and Variables,” Technical Report MS-CIS-8906, Philadelphia, PA: University of Pennysylvania Computer and Information Science Department.

    Google Scholar 

  • Shastri, L. and Ajjanagadde, V. (1993), “From Simple Associations to Systematic Reasoning: A Connectionist Representation of Rules, Variables, and Dynamic Bindings using Temporal Synchrony,” Behavioral and Brain Sciences, vol. 16, no. 3.

    Google Scholar 

  • Sun, R. 1991, “Integrating Rules and Connectionism for Robust Reasoning,” in Technical Report No. CS-90-154, Waltham, MA: Brandeis University. Computer Science Dept.

    Google Scholar 

  • Sun, R. (1994), “CONSYDERR: A Two Level Hybrid Architecture for Structuring Knowledge for Commonsense Reasoning,” Proceedings of the First International Symposium on Integrating Knowledge and Neural Heuristics, Florida, USA, pp. 32–39.

    Google Scholar 

  • Sulzberger, S.N., Tschichold-Giirman, N., and Vestli, S.J. (1993), “FUN: Optimization of Fuzzy Rule Based Systems using Neural Networks,” Proceedings of the IEEE International Conference on Neural Networks, San Francisco, vol. 1, pp. 312–316.

    Google Scholar 

  • Takagi, H., and Sugeno, M. (1985), “Fuzzy Identification of Systems and its Applications to Modeling and Control,” IEEE Transactions on Systems Man and Cybernetics, vol. 15, no. 1 pp. 116–132.

    Article  MATH  Google Scholar 

  • Takagi, H., and Hayashi, I. (1991), “NN-driven Fuzzy Reasoning,” International Journal of Approximate Reasoning, vol. 5, pp. 191–212

    Article  MATH  Google Scholar 

  • Takagi, H., and Hayashi, I. (1992a), “NN-driven Fuzzy Reasoning,” Fuzzy Models of Pattern Recognition, edited by Bezdek and Pal, IEEE Press, pp. 496–512.

    Google Scholar 

  • Takagi, H., and Hayashi, I. (1992b), “Neural Networks Designed on Approximate Reasoning Architecture and their Applications,” IEEE Transactions on Neural Networks, September, vol. 2, no. 5, pp.752–760.

    Article  Google Scholar 

  • Lee, M. A., and Takagi, H. (1993), “Integrating Design Stages of Fuzzy System using Genetic Algorithms,” 1993 IEEE International Conference on Fuzzy Systems using Genetic Algorithms vol. 1, pp. 612–617. bibitemTakeda94 Takeda, F., and Omatu, S. (1994), “A Neuro-Money Recognition Using Optimized Masks by GA,” Advances in Fuzzy Logic, Neural Networks and Genetic Algorithms, pp. 190–202.

    Google Scholar 

  • Towell, G. and Shavlik, J. (1992), “The Extraction of Refined Rules from Knowledge-based Neural Networks,” Machine Learning Research Group Working Paper 91-4, University of Wisconsin, Madison, USA.

    Google Scholar 

  • Touretzky, D. S. (1987), “Representing Conceptual Structures in a Neural Network,” Proceedings of the First IEEE Conference on Neural Networks, San Diego, USA.

    Google Scholar 

  • Touretzky, D. S., and Hinton, G. E. (1988), “A Distributed Connectionist Production System,” Cognitive Science, vol. 12, pp. 423–466.

    Article  Google Scholar 

  • Watnabe, T., Matsumoto, M., and Hasegawn, T. (1990), “A Layered Neural Network Model using Logic Neurons,” Proceedings of the International Conference on Fuzzy Logic & Neural Networks, Iizuka, Japan, pp. 675–678.

    Google Scholar 

  • Whitley, D., Strakweather, T., and Bogart, C. (1990), “Genetic Algorithms and Neural Networks: Optimizing Connection and Connectivity,” Parallel Computing, vol. 14 pp. 347–361.

    Article  Google Scholar 

  • Widrow, B., Winter, R. G., and Baxter, R. A. (1988), “Layered Neural Nets for Pattern Recognition,” IEEE Transactions on Acoustics, Speech and Signal Processing, vol. 36, no. 7, pp. 1109–1118.

    Article  MATH  Google Scholar 

  • Williams, B., Bostock, R., Bounds, D., and Harget, A. (1994), “Improving Classification Performance in the Bumptree Network by Optimizing Topology with a Genetic Algorithm,” Proceedings of the First IEEE Conference on Evolutionary Computation, vol. 1 pp. 490–495.

    Google Scholar 

  • Zadeh, L.A. (1965), “Fuzzy Sets,” Information and Control, vol. 8, pp. 338–353.

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. La Trobe University, Melbourne, Australia

    Rajiv Khosla & Tharam Dillon

Authors
  1. Rajiv Khosla
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tharam Dillon
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

Copyright information

© 1997 Springer Science+Business Media New York

About this chapter

Cite this chapter

Khosla, R., Dillon, T. (1997). Intelligent Fusion and Transformation Systems. In: Engineering Intelligent Hybrid Multi-Agent Systems. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-6223-8_3

Download citation

  • DOI: https://doi.org/10.1007/978-1-4615-6223-8_3

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4613-7854-9

  • Online ISBN: 978-1-4615-6223-8

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation