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.
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.
The NNC helped organize the first IEEE World Congress on Computational Intelligence in Orlando, Florida in 1994.
[34] Therefore, fuzzy logic is well suited for engineering decisions without clear certainties and uncertainties or with imprecise data - as with natural language-processing technologies[35] but it doesn't have learning abilities.
[36] This technique tends to apply to a wide range of domains such as control engineering,[37] image processing,[38] fuzzy data clustering[38][39] and decision making.
[35] Fuzzy logic-based control systems can be found, for example, in the field of household appliances in washing machines, dish washers, microwave ovens, etc.
Other areas such as medical diagnostics, satellite controllers and business strategy selection are just a few more examples of today's application of fuzzy logic.
The main advantages of this technology therefore include fault tolerance, pattern recognition even with noisy images and the ability to learn.
[45][43][41] The numerous applications include, for example, the analysis and classification of medical data, including the creation of diagnoses, speech recognition, data mining, image processing, forecasting, robot control, credit approval, pattern recognition, face and fraud detection and dealing with nonlinearities of a system in order to control it.
Generative systems based on deep learning and convolutional neural networks, such as chatGPT or DeepL, are a relatively new field of application.
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).
[51] Being one of the main elements of fuzzy logic, probabilistic methods firstly introduced by Paul Erdos and Joel Spencer (1974),[52] aim to evaluate the outcomes of a Computation Intelligent system, mostly defined by randomness.
[54] All the major academic publishers are accepting manuscripts in which a combination of Fuzzy logic, neural networks and evolutionary computation is discussed.
