Computational intelligence
[1] These systems are aimed at mastering complex tasks in a wide variety of technical or commercial areas and offer solutions that recognize and interpret patterns, control processes, support decision-making or autonomously manoeuvre vehicles or robots in unknown environments, among other things.
[2] These concepts and paradigms are characterized by the ability to learn or adapt to new situations, to generalize, to abstract, to discover and to associate.
Sometimes data-driven methods are suitable for finding a good model and sometimes logic-based knowledge representations deliver better results.
It requires a precisely stated analytical model of the task to be processed and a prewritten program, i.e. a fixed set of instructions.
When applied to real-world tasks, systems based on HC result in specific control actions defined by a mathematical model or algorithm.
[23][24][25][26] Soft computing, on the other hand, is based on the fact that the human mind is capable of storing information and processing it in a goal-oriented way, even if it is imprecise and lacks certainty.
For hard problems for which no satisfying exact solutions based on HC are available, SC methods can be applied successfully.
SC methods are usually stochastic in nature i.e., they are a randomly defined processes that can be analyzed statistically but not with precision.
[34] It can face incompleteness, and most importantly ignorance of data in a process model, contrarily to Artificial Intelligence, which requires exact knowledge.
Other areas such as medical diagnostics, foreign exchange trading and business strategy selection are apart from this principle's numbers of applications.
Fuzzy logic is mainly useful for approximate reasoning, and doesn't have learning abilities,[35] a qualification much needed that human beings have.
[37] Concerning its applications, neural networks can be classified into five groups: data analysis and classification, associative memory, clustering generation of patterns and control.
[37] Generally, this method aims to analyze and classify medical data, proceed to face and fraud detection, and most importantly deal with nonlinearities of a system in order to control it.
Evolutionary computation can be seen as a family of methods and algorithms for global optimization, which are usually based on a population of candidate solutions.
In psychology, learning is the process of bringing together cognitive, emotional and environmental effects and experiences to acquire, enhance or change knowledge, skills, values and world views (Ormrod, 1995; Illeris, 2004).
[44] Being one of the main elements of fuzzy logic, probabilistic methods firstly introduced by Paul Erdos and Joel Spencer (1974),[45] aim to evaluate the outcomes of a Computation Intelligent system, mostly defined by randomness.
[47] All the major academic publishers are accepting manuscripts in which a combination of Fuzzy logic, neural networks and evolutionary computation is discussed.
