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Advanced Intelligent Systems
Prof. Rushen Chahal 12-1
Prof. Rushen Chahal
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Learning Objectives
Understand second-generation intelligentsystems.
Learn the basic concepts and applications ofcase-based systems.
Understand the uses of artificial neural networks.
Examine the advantages and disadvantages ofartificial neural networks.
Learn about genetic algorithms.
Examine the theories and applications of fuzzyknowledge.
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Household Financials Vision Speeds Loan
Approvals With Neural Networks Vignette
Loan product regulation varies in each state
Develop an object-oriented loan approval system Neural network-based
Fed risk, interest rate variables, customer data
Estimates credit worthiness, potential for fraud
Pattern recognition
Integrates all loan approval phases
Uses intelligent underwriting engine
Reduced training time and administrative overhead
Decreased managed basis efficiency ratio Upgradeable to web-based architecture
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Machine Learning
Acquisition of knowledge through historical
examples
Implicitly induces expert knowledge from history
Different from the way that humans learn
Implications of system success and failure
unclear
Manipulates of symbols instead of numbers
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Methods
Supervised learning Induce knowledge from known outcomes
New cases used to modify existing theories
Statistical methods
Rule induction Case based and inference
Neural computing
Genetic algorithms leading to survival of fittest
Unsupervised learning
Determine knowledge from data with unknown outcomes Clustering data into similar groups
Neural computing
Genetic algorithms leading to survival of fittest
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Case Reasoning
Inductive
Case base used for decision-making
Effective when rule-based reasoning is not Case
Primary knowledge element Ossified
Paradigmatic Stories
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Human Brain
50 to 150 billion neurons in brain
Neurons grouped into networks Axons send outputs to cells
Received by dendrites, across synapses
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Neural Networks
Attempts to mimic brain functions
Analogy, not accurate model
Artificial neurons connected in network Organized by topologies Structure
Three or more layers
Input, intermediate (one or more hidden layers), output Receives modifiable signals
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Network Learning
Learning algorithms Supervised
Connection weights derived from known cases
Pattern recognition combined with weighting changes
Back error propagation Easy implementation
Multiple hidden layers
Adjust learning rate and momentum
Known patterns compared to output and allows for weight adjustment
Established error tolerance
Unsupervised
Only stimuli shown to network Humans assign meanings and determine usefulness
Adaptive resonance theory
Kohonen self-organizing feature maps
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Development of Systems
Collect data The more, the better
Separate data into training set to adjust weights
Divide into test sets for network validation
Select network topology Determine input, output, and hidden nodes, and hidden layers
Select learning algorithm and connection weights
Iterative training until network achieves preset error level
Black box testing to verify inputs produce appropriate outputs Contains routine and problematic cases
Implementation Integration with other systems User training
Monitoring and feedback
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Genetic Algorithms
Computer programs that apply processes ofevolution Viability of candidate solutions
Self-organized
Adaptable
Fitness function Measured by objective obtained
Iterative process Candidate solutions combine to produce generations
Reproduction, crossover, mutation
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Genetic Algorithms
Establish problem Parameters
Number of initial solutions, number of offspring, number of parentsand offspring for each generation, mutation level, probabilitydistribution of crossover point occurrence
Generate initial set of solutions Compute fitness functions
Total all fitness functions
Compare each solutions fitness function to total
Apply crossover
Apply random mutation
Repeat until good enough solution or no improvement
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Fuzzy Logic
Mathematical theory of fuzzy sets
Imprecise thinking
Describes human perception
Continuous logic Not 100% true or false, black or white
Fuzzy neural networks Fuzzification
Fuzzy logic applied to input and output used to create model
Defuzzification Model converted back to original input, output scales
Output becomes input for another intelligent system
Prof. Rushen Chahal 12-20
