Soft computing: Difference between revisions

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== History ==

The development of soft computing dates back to the late 20th century. In 1965, [[Lotfi A. Zadeh|Lotfi Zadeh]] introduced fuzz logic, which laid the mathematical groundwork for soft computing. Between the 1960s and 1970s, evolutionary computation, the development of [[Geneticgenetic algorithm|genetic algorithms]]s that mimicked biological processes, began to emerge. These models carved the path for models to start handling uncertainty. Although neural network research began in the 1940s and 1950s, there was a new demand for research in the 1980s. Researchers invested time to develop models for [[pattern recognition]]. Between the 1980s and 1990s, hybrid intelligence systems merged fuzz logic, neural networks, and evolutionary computation that solved complicated problems quickly. From the 1990s to the present day, Models have been instrumental and affect multiple fields handling [[big data]], including engineering, medicine, social sciences, and finance.<ref>Chaturvedi, Devendra K. "Soft computing." Studies in Computational intelligence 103 (2008): 509-612.</ref><ref name=":0">{{Cite book |last1=Ram |first1=Mangey |url=https://books.google.com/books?id=qjL3DwAAQBAJ&dq=history%20of%20soft%20computing&pg=PA74 |title=Advanced Mathematical Techniques in Engineering Sciences |last2=Davim |first2=J. Paulo |date=2018-05-04 |publisher=CRC Press |isbn=978-1-351-37189-6 |language=en}}</ref>

== Computational techniques ==

=== Fuzzy logic ===

[[Fuzzy logic]] is an aspect of computing that handles approximate reasoning. Typically, [[Boolean algebra|binary logic]] allows computers to make decisions on true or false reasons (0s and 1s); however, introducing fuzzy logic allows systems to handle the unknowns between 0 and 1.<ref name="Procedia" /><ref>{{Cite web |date=2018-04-10 |title=Fuzzy Logic {{!}} Introduction |url=https://www.geeksforgeeks.org/fuzzy-logic-introduction/ |access-date=2023-11-11 |website=GeeksforGeeks |language=en-US}}</ref>

Unlike [[Classical set theory|classical sets]] that allow members to be entirely within or out, fuzzy sets allow partial membership by incorporating "graduation" between sets. Fuzzy logic operations include [[negation]], conjunction, and [[Logical disjunction|disjunction]], which handle membership between data sets.<ref name=":0" />

Fuzzy rules are logical statements that map the correlation between input and output parameters. They set the rules needed to trace variable relationships linguistically, and they would not be possible without [[linguistic variables]]. Linguistic variables represent values typically not quantifiable, allowing uncertainties.<ref> Trillas, Enric, and Luka Eciolaza. "Fuzzy logic." Springer International Publishing. DOI 10 (2015): 978-3. </ref>

=== Neural networks ===

[[Neural network]]s are computational models that attempt to mimic the structure and functioning of the [[human brain]]. While computers typically use [[Boolean algebra|binary logic]] to solve problems, neural networks attempt to provide solutions for complicated problems by enabling systems to think human-like, which is essential to soft computing.<ref name=":2">{{Cite web |title=Model Compression and Acceleration for Deep Neural Networks: The Principles, Progress, and Challenges {{!}} IEEE Journals & Magazine {{!}} IEEE Xplore |url=https://ieeexplore.ieee.org/document/8253600/;jsessionid=Es-8JJ2aTxyDbz-ZeAW6ojB2bGom7NU413NP86MhLqTbzB3fmAGf!-668841979 |access-date=2023-11-11 |website=ieeexplore.ieee.org}}</ref>

Neural networks revolve around [[Perceptron|perceptronsperceptron]]s, which are [[Artificialartificial neuron|artificial neurons]]s structured in layers. Like the human brain, these interconnected nodes process information using complicated mathematical operations.<ref>{{Cite web |title=What are Neural Networks? {{!}} IBM |url=https://www.ibm.com/topics/neural-networks |access-date=2023-11-11 |website=www.ibm.com |language=en-us}}</ref>

Through training, the network handles input and output data streams and adjusts parameters according to the provided information. Neural networks help make soft computing extraordinarily flexible and capable of handling high-level problems.

In soft computing, neural networks aid in pattern recognition, predictive modeling, and data analysis. They are also used in [[image recognition]], [[natural language processing]], [[speech recognition]], and [[System|systemssystem]]s.<ref name=":1" /><ref name=":3">{{Cite web |title=IEEE Xplore Full-Text PDF |url=https://ieeexplore.ieee.org/document/8859190 |access-date=2023-11-11 |website=ieeexplore.ieee.org}}</ref>

=== Evolutionary computation ===

[[Evolutionary computation]] is a field in soft computing that uses the principles of [[natural selection]] and [[evolution]] to solve complicated problems. It promotes the discovery of diverse solutions within a solution space, encouraging near-perfect solutions. It finds satisfactory solutions by using computational models and types of evolutionary algorithms. Evolutionary computation consists of algorithms that mimic natural selection, such as [[Geneticgenetic algorithm|genetic algorithms]]s, [[genetic programming]], and [[evolutionary programming]]. These algorithms use [[Crossover (genetic algorithm)|crossover]], [[Mutation (genetic algorithm)|mutation]], and [[Selection (genetic algorithm)|selection]].<ref>{{Cite web |date=2017-06-29 |title=Genetic Algorithms |url=https://www.geeksforgeeks.org/genetic-algorithms/ |access-date=2023-11-11 |website=GeeksforGeeks |language=en-US}}</ref>

Crossover, or recombination, exchanges data between nodes to diversify data and handle more outcomes. [[Mutation]] is a genetic technique that helps prevent the premature conclusion to a suboptimal solution by diversifying an entire range of solutions. It helps new optimal solutions in solution sets that help the overall optimization process. Selection is an operator that chooses which solution from a current population fits enough to transition to the next phase. These drive genetic programming to find optimal solutions by ensuring the survival of only the fittest solutions in a set.

In soft computing, evolutionary computation helps applications of [[data mining]] (using large sets of data to find patterns), [[robotics]], optimizing, and engineering methods.<ref name=":1" /><ref name=":0" />

=== Hybrid intelligence systems ===

Hybrid intelligence systems combine the strengths of soft computing components to create integrated computational models. Artificial techniques such as fuzzy logic, neural networks, and evolutionary computation combine to solve problems efficiently. These systems improve judgment, [[troubleshooting]], and [[data analysis]]. Hybrid intelligence systems help overcome the limitations of individual AI approaches to improve performance, accuracy, and adaptability to address [[Dynamic problem (algorithms)|dynamic problems]]. It advances soft computing capabilities in data analysis, pattern recognition, and systems.<ref name=":4">{{Cite book |last=Medsker |first=Larry R. |url=https://books.google.com/books?id=EXngBwAAQBAJ&q=evolutionary&pg=PR13 |title=Hybrid Intelligent Systems |date=2012-12-06 |publisher=Springer Science & Business Media |isbn=978-1-4615-2353-6 |language=en}}</ref>

== Applications ==

Due to their dynamic versatility, soft computing models are precious tools that confront complex real-world problems. They are applicable in numerous industries and research fields:

Soft computing fuzzy logic and neural networks help with pattern recognition, image processing, and computer vision. Its versatility is vital in [[natural language processing]] as it helps decipher human emotions and language. They also aid in data mining and [[Predictive analytics|predictive analysis]] by obtaining priceless insights from enormous datasets. Soft computing helps optimize solutions from energy, [[Financialfinancial forecast|financial forecasts]]s, environmental and biological data modeling, and anything that deals with or requires models.<ref name=":4" /><ref>{{Cite web |title=Industrial applications of soft computing: a review {{!}} IEEE Journals & Magazine {{!}} IEEE Xplore |url=https://ieeexplore.ieee.org/document/949483/;jsessionid=xdS8IFFQN8YRhXQajnUBK1GxF5Fzj_edYcUsqEW5vE3xWwb3XJ8G!-1911429853 |access-date=2023-11-11 |website=ieeexplore.ieee.org}}</ref>

Within the medical field, soft computing is revolutionizing disease detection, creating plans to treat patients and models of [[Health care|healthcare]].<ref name=":3" />

== Challenges and limitations ==

Soft computing methods such as neural networks and fuzzy models are complicated and may need clarification. Sometimes, it takes effort to understand the logic behind neural network algorithms' decisions, making it challenging for a user to adopt them. In addition, it takes valuable, costly resources to feed models extensive data sets, and sometimes it is impossible to acquire the computational resources necessary. There are also significant hardware limitations which limits the computational power.<ref name=":2" />

Furthermore, there needs to be more backing behind soft computing algorithms, which makes them less reliable than complicated computing models. Finally, there is a considerable potential for bias because of the input data, which leads to ethical dilemmas if methods are in fields such as medicine, finance, and healthcare.

== References ==