Hybrid systems

Department of Information Technology 1Soft Computing (ITC4256 )
Dr. C.V. Suresh Babu
Professor
Department of IT
Hindustan Institute of Science & Technology
Hybrid Systems

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

 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

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

 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

 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.

Comparison of Expert Systems, Fuzzy Systems,
Neural Networks and Genetic Algorithms
Knowledge representation
Uncertainty tolerance
Imprecision tolerance
Adaptability
Learning ability
Explanation ability
Knowledge discovery and data mining
Maintainability
ES FS NN GA
  

  
 
 







* The terms used for grading are:
- bad, - rather bad, - good - rather good and 

 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

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).

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.

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.

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.

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.

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

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

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

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

Generalized Fused Framework

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 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.

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

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

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.

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.

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.

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

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.

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.

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.

Hybrid systems

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