Foundations. Computational Intelligence

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1 Foundations Computational Intelligence

2 Intelligence A standard dictionary definition of intelligence is: "1 a (1): The ability to learn or understand or to deal with new or trying situations : REASON; also : the skilled use of reason (2): the ability to apply knowledge to manipulate one's environment or to think abstractly as measured by objective criteria (as tests)."

3 Intelligence According to David Fogel (1995): "Intelligence is the capability of a system to adapt its behavior to meet its goals in a range of environments. It is a property of all purpose-driven decision-makers."

4 Artificial neural network An artificial neural network (ANN) is an analysis paradigm that is roughly modeled after the massively parallel structure of the brain. It simulates a highly interconnected, parallel computational structure with many relatively simple individual processing elements (PEs).

5 Fuzzy logic Is the logic of "approximate reasoning." It comprises operations on fuzzy sets including equality, containment, complementation, intersection, and union; it is a generalization of conventional (twovalued, or crisp) logic.

6 Evolutionary computation Evolutionary computation comprises machine learning optimization and classification paradigms roughly based on mechanisms of evolution such as biological genetics and natural selection. All its paradigms use populations of individuals (potential solutions), rather than single data points or vectors. GA EP ES GP

7 Computational Intelligence It is a methodology involving computing that provides a system with an ability to learn and/or to deal with new situations, such that the system is perceived to possess one or more attributes of reason, such as generalization, discovery, association, and abstraction. They are often designed to mimic one or more aspects of biological intelligence. CI = ANN + Fuzzy + EC + Knowledge CI = Adaptation

8 A shorter definition of CI It comprises practical adaptation concepts, paradigms, algorithms, and implementations that enable or facilitate appropriate actions (intelligent behavior) by systems in complex and changing environments.

9 Biological Basis for Neural Network

10 The Neuron The human brain has a large number of neurons, or processing elements (PEs). Typical estimates of the total number are on the order of 10 to 500 billion (Rumelhart and McClelland 1986). According to Stubbs (1988), neurons are arranged into about 1,000 main modules, each with about 500 neural networks. Each network has on the order of 100,000 neurons. The axon of each neuron connects to anywhere from hundreds to thousands of other neurons; the value varies greatly from neuron to neuron and from neuron type to neuron type. According to a rule called Eccles's law, each neuron either excites or inhibits all neurons to which it is connected.

11 Neuron

12 Differences among ANN and BNN

13 Differences between ANN and BNN In a typical implementation of an ANN, connections among PEs can have either positive or negative weights. BPE 10 to 100 milliseconds while APE 10 to 100 nanoseconds. ANN contains a few dozen to hundreds of PE while each of the 1,000 main modules in the human brain described by Stubbs (1988) contains about 500 million neurons.

14 Biological basis for evolutionary computation

15 Chromosomes They are structures in cell nuclei (cell bodies) that transmit genetic information.

16 Chromosomes and other hallucinogen terms The collection of chromosomes required to completely specify an organism is called the genotype. They are made up of genes, each of which is identified by its location (locus) and its function, such as a person's hair color gene. In other words, genes are specific segments of chromosomes associated with specific functions. Individual values a gene may assume are called alleles; a hair color allele value may be "brown hair."

17 Chromosomes and other hallucinogen terms Biological chromosomes contain linear threads of DNA, nucleic acids that make up an extremely complex double helix structure. The biological chromosomes that define an organism vary in length, although a specific chromosome is generally the same length from one organism to another. Biological chromosomes duplicate themselves during cell division, which occurs during a normal cell's lifetime. Many cell divisions (duplications) occur within an organism for every event of sexual reproduction.

18 Behavioral motivation of fuzzy logic

19 Logic vs Fuzzy logic We do not live in a world of ones and zeros, black and white, true and false, or other absolutes. Our live include a large measure of uncertainty. Two main types of uncertainty exist: statistical and nonstatistical (which is based on vagueness, imprecision, and/or ambiguity).

20 Myths about CI You need a Xiuhcoatl to run the paradigms. They can solve all the difficult engineering problems No programming is needed It is necessary to know everything about neuro biology or biological genetics Fuzzy logic is really fuzzy. CI finds absolute minimum problems

21 Application areas

22 Neural networks Classification Content addressable or associative memory: This process is sometimes described as obtaining an exemplar pattern from a noisy and/or incomplete one. Clustering or compression Control systems Generation of sequence of patterns

23 Evolutionary Computation Optimization Classification

24 Control Systems Expert systems Fuzzy Logic

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