Hybrid systems

This presentation discusses about the following topics:
Hybrid Systems
Hybridization
Combinations
Comparison of Expert Systems, Fuzzy Systems, Neural Networks and Genetic Algorithms
Current Progress
Primary Components
MultiComponents
Degree of Integration
Transformational, hierarchial and integrated
Stand Alone Models
Integrated – Fused Architectures
Generalized Fused Framework
System Types for Hybridization

1. Dr . C.V. Suresh Babu Professor Department of IT Hindustan Institute of Science & Technology Hybrid Systems

2. Action Plan Hybrid Systems Hybridization Combinations Comparison of Expert Systems, Fuzzy Systems, Neural Networks and Genetic Algorithms Current Progress Primary Components MultiComponents Degree of Integration Transformational, hierarchial and integrated Stand Alone Models Integrated – Fused Architectures Generalized Fused Framework System Types for Hybridization Quiz

3. A hybrid intelligent system is one that combines at least two intelligent technologies. For example, combining a neural network with a fuzzy system results in a hybrid neuro -fuzzy system. The combination of: probabilistic reasoning, fuzzy logic, neural networks and evolutionary computation forms the core of soft computing , Soft Computing is an emerging approach to building hybrid intelligent systems capable of reasoning and learning in an uncertain and imprecise environment . Hybrid Systems

4. Hybridization Integrated architectures for machine learning have been shown to provide performance improvements over single representation architectures. Integration, or hybridization, is achieved using a spectrum of module or component architectures ranging from those sharing independently functioning components to architectures in which different components are combined in inherently inseparable ways. In this presentation we briefly survey prototypical integrated architectures

5. Although words are less precise than numbers , precision carries a high cost. We use words when there is a tolerance for imprecision . Soft computing exploits the tolerance for uncertainty and imprecision to achieve greater tractability and robustness, and lower the cost of solutions. We also use words when the available data is not precise enough to use numbers. This is often the case with complex problems, and while “hard” computing fails to produce any solution, soft computing is still capable of finding good solutions. Using “words” rather than strict numbers

6. Lotfi Zadeh is reputed to have said that a good hybrid would be “British Police, German Mechanics, French Cuisine, Swiss Banking and Italian Love”. But “British Cuisine, German Police, French Mechanics, Italian Banking and Swiss Love” would be a bad one. Likewise, a hybrid intelligent system can be good or bad – it depends on which components constitute the hybrid. So our goal is to select the right components for building a good hybrid system.

7. Comparison of E xpert Systems, F uzzy Systems, N eural Networks and G enetic Algorithms

8. The combination of knowledge based systems, neural networks and evolutionary computation forms the core of an emerging approach to building hybrid intelligent systems capable of reasoning and learning in an uncertain and imprecise environment. Combinations

9. Current Progress In recent years multiple module integrated machine learning systems have been developed to overcome the limitations inherent in single component systems. Integrations of neural networks (NN), fuzzy logic (FL) and global optimization algorithms have received considerable attention but increasing attention is being paid to integrations with case based reasoning (CBR) and rule induction (RI ).

10. Primary Components The full spectrum of knowledge representation in such systems is not confined to the primary components. For example, in CBR systems although much knowledge resides in the case library significant problem solving knowledge may reside in secondary technologies such as in the similarity metric used to retrieve problem solution pairs from the case library, in the adaptation mechanisms used to improve an approximate solution and in the case library maintenance mechanisms.

11. MultiComponents Although it is possible to generalize about the relative utilities of these component types based on the primary knowledge representation mechanisms these generalizations may no longer remain valid in particular cases depending on the characteristics of the secondary mechanisms employed. Table 1 attempts to gauge the relative utilities of single components systems based on the primary knowledge representation.

12. Degree of Integration Besides differing in the types of component systems employed, different integrated architectures have emerged in a rather ad hoc way. Least integrated architectures consisting of independent components communicating with each other on a side by side basis. More integration is shown in transformational or hierarchial systems in which one technique may be used for development and another for delivery or one component may be used to optimize the performance of another component. More fully integrated architectures combine different effects to produce a balanced overall computational model.

13. Transformational, hierarchial and integrated This categorizeses such systems as transformational, hierarchial and integrated. In a transformational integrated system the system may use one type of component to produce another which is the functional system. For example, a rule based system may be used to set the initial conditions for a neural network solution to a problem. Thus, to create a modern intelligent system it may be necessary to make a choice of complementary techniques.

14. Stand Alone Models Independent components that do not interact Solving problems that have naturally independent components – eg ., decision support and categorization

15. Transformational Expert systems with neural networks Knowledge from the ES is used to set the initial conditions and training set of the NN

16. Hierarchial Hybrid An ANN uses a GA to optimize its topology and the output fed into an ES which creates the desired output or explanation

17. Integrated – Fused Architectures Combine different techniques in one computational model Share data structures and knowledge representations Extended range of capabilities – e.g., classification with explanation, or, adaptation with classification

18. Generalized Fused Framework

19. Fused Architecture The architecture consists of four components and the environment. The performance element (PE) is the actual controller. The learning element.(LE) updates the knowledge in the PE . The LE has access to the environment, the past states and the performance measure. It updates the PE. The critic examines the external performance and provides feedback to the LE. The critic faces the problem of converting an external reinforcement into an internal one. The problem generator is to contribute to the exploration of the problem space in an efficient way. The framework does not specify the techniques.

20. System Types for Hybridization Knowledge-based Systems and if-then rules CBR Systems Evolutionary Intelligence and Genetic algorithms Artificial Neural Networks and Learning Fuzzy Systems PSO Systems

21. Knowledge in Intelligent Systems In rule induction systems knowledge is represented explicitly by if-then rules that are obtained from example sets. In neural networks knowledge is captures in synaptic weights in systems of neurons that capture categorizations in data sets. In evolutionary systems knowledge is captured in evolving pools of selected genes and in heuristics for selection of more adapted chromosomes. In case based systems knowledge is primarily stored in the form of case histories that represent previously developed problem-solution pairs. In PSO systems the knowledge is stored in the prticle swarms

22. CBR KB NN GA FL Know. rep. 3 4 1 2 4 Uncertainty 1 1 4 4 4 Approximation (noisy incomplete data) 1 1 4 4 4 Adaptable 4 2 4 4 2 Learnable 3 1 4 4 2 Interpretable 3 4 1 2 4 Table 1 (Adapted from [Abr, Jac] and [Neg]). A comparison of the utility of case based reasoning systems (CBR), rule induction systems (RI), neural networks (NN) genetic algorithms (GA) and fuzzy systems (FS), with 1 representing low and 4 representing a high utility.

23. Interpretability Synaptic weights in trained neural networks are not easy to interpret with particular difficulties if interpretations are required. Genetic algorithms model natural genetic adaptation to changing environments and thus are inherently adaptable and learn well Not easily interpretable because although the knowledge resides partly in the selection mechanism it is in the most part deeply embedded within a population of adapted genes.

24. Adaptability Case based systems are adaptable because changing the case library may be sufficient to port a system to a related area. If changes need to be made to the similarity metric or the adaptation mechanism or if the case structure needs to be changed much more work may be required.

25. Learnability Fuzzy rule based systems offer more option through which learnability may be more easily achieved. Fuzzy rules may be fine tuned by adjusting the shapes of the fuzzy sets according to user feedback

26. Rules and cases Rule based systems employ an easily comprehensible but rigid representation of expert knowledge such systems may afford better interpretation mechanisms. Similarly recent research shows [SØR] that explanation techniques for large case bases is most promising while case based learning and maintenance can often be very efficient because of the transparency of typical case libraries.

27. Test Yourself 1. When it comes to the areas of data and knowledge, computers are much better at handling: A. knowledge first, then processing the data. B. knowledge than data. C. data than knowledge. D. only knowledge. 2. When a computer can correctly recognize faces of users with a high degree of reliability, it is using: A. fuzzy logic. B. pattern recognition. C. image analysis. D. OCR. 3. A software program designed to replicate the decision-making process of a human expert is a(n): A. data system. B. database. C. expert system. D. semantic system. 4. When a conclusion is stated as a probability rather than an exact fact, it is known as: A. an expert system. B. a database. C. fuzzy logic. D. a pattern recognition system 5. Expert systems primarily started in the: A. insurance field. B. medical field. C. aviation field. D. library reference field.

28. Answers 1. When it comes to the areas of data and knowledge, computers are much better at handling: A. knowledge first, then processing the data. B. knowledge than data. C. data than knowledge. D. only knowledge . 2. When a computer can correctly recognize faces of users with a high degree of reliability, it is using : A. fuzzy logic. B. pattern recognition. C. image analysis. D. OCR . 3. A software program designed to replicate the decision-making process of a human expert is a(n ): A. data system. B. database. C. expert system. D. semantic system . 4. When a conclusion is stated as a probability rather than an exact fact, it is known as: A. an expert system. B. a database. C. fuzzy logic. D. a pattern recognition system 5. Expert systems primarily started in the: A. insurance field. B. medical field. C. aviation field. D. library reference field.