Soft Computing

Soft Computing

Soft Computing is a component of computer science. Essentially, it is a type of algorithm that produces approximate solutions to high-level problems which might prove to be unsolvable. Typically, traditional hard-computing algorithms heavily rely on concrete data and mathematical models in order to produce solutions to problems. Soft computing is designed to handle incomplete problems where there is a lack of data, variability, and obscure problems.

In the context of artificial intelligence, soft computing provides tools to handle real-world uncertainties. Its methods supplement preexisting methods for better solutions. The combination with artificial intelligence has led to hybrid-intelligence systems that merge various computational algorithms. Expanding the applications of artificial intelligence, soft computing leads to powerful solutions. Key points include: tackling ambiguity, flexible learning, grasping intricate data, real-world applications, and ethical artificial intelligence.

History

The development of soft computing dates back to the late 20th century.

Fuzz Logic (1965)

Lotfi Zadeh introduced fuzz logic that laid the mathematical groundwork for soft computing.

Nueral Networks (1980's)

Although neural network research began in the 40's and 50's, there was a new demand for neural network research in the 80's. Researchers invested time to develop models for pattern recognition.

Evolutionary Computation ( 1960's-1970's)

Development of genetic algorithms that mimicked biological processes began to emerge. These models carved the path for models to start handling uncertainty.

Integration( 1980's - 1990's)

Existing models began to merge with genetic algorithms, fuzzy logic, and neural networks in order to create hybrid-intelligence systems that were better equipped to solve complicated problems.

Diversification (1990's- present day)

Models have been instrumental and affect multiple fields handling big data including: engineering, medicine, social sciences, and finance.

Fuzzy Logic

Fuzzy Logic is an aspect of computing that handles approximate reasoning. Typically, binary logic solely allows computers to make decisions on true or false reasons (0's and 1's); however, the introduction of fuzzy logic allows systems to handle the unknowns between 0 and 1.

Unlike classical sets that allow members to either be completely within the set or out, fuzzy sets allow partial membership by incorporating "graduation" between sets. Fuzzy logic operations include negation, conjunction, and disjunction, which allow for the handling of membership between sets of data.

Fuzzy rules are logical statements that map the correlation between input and output parameters. They set the rules needed to linguistically trace variable relationships. Would not be possible without linguistic variables. Representing values that are typically not quantifiable, linguistic variables allow uncertainties to be entertained.

Neural Networks

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