- Добавил: literator
- Дата: 23-12-2022, 03:27
- Комментариев: 0
Название: Handbook of HydroInformatics: Volume I: Classic Soft-Computing Techniques
Автор: Saeid Eslamian, Faezeh Eslamian
Издательство: Elsevier
Год: 2023
Страниц: 484
Язык: английский
Формат: pdf (true)
Размер: 43.1 MB
Classic Soft-Computing Techniques is the first volume of the three, in the Handbook of HydroInformatics series. Through this comprehensive, 34-chapters work, the contributors explore the difference between traditional computing, also known as hard computing, and soft computing, which is based on the importance given to issues like precision, certainty and rigor. The chapters go on to define fundamentally classic soft-computing techniques such as Artificial Neural Network, Fuzzy Logic, Genetic Algorithm, Supporting Vector Machine, Ant-Colony Based Simulation, Bat Algorithm, Decision Tree Algorithm, Firefly Algorithm, Fish Habitat Analysis, Game Theory, Hybrid Cuckoo–Harmony Search Algorithm, Honey-Bee Mating Optimization, Imperialist Competitive Algorithm, Relevance Vector Machine, etc. The Chapter 1 describes different types of regressions and how they can be used to solve real problems. To solve our challenges in different sections, we utilize the Scikit-learn Python library.
Автор: Saeid Eslamian, Faezeh Eslamian
Издательство: Elsevier
Год: 2023
Страниц: 484
Язык: английский
Формат: pdf (true)
Размер: 43.1 MB
Classic Soft-Computing Techniques is the first volume of the three, in the Handbook of HydroInformatics series. Through this comprehensive, 34-chapters work, the contributors explore the difference between traditional computing, also known as hard computing, and soft computing, which is based on the importance given to issues like precision, certainty and rigor. The chapters go on to define fundamentally classic soft-computing techniques such as Artificial Neural Network, Fuzzy Logic, Genetic Algorithm, Supporting Vector Machine, Ant-Colony Based Simulation, Bat Algorithm, Decision Tree Algorithm, Firefly Algorithm, Fish Habitat Analysis, Game Theory, Hybrid Cuckoo–Harmony Search Algorithm, Honey-Bee Mating Optimization, Imperialist Competitive Algorithm, Relevance Vector Machine, etc. The Chapter 1 describes different types of regressions and how they can be used to solve real problems. To solve our challenges in different sections, we utilize the Scikit-learn Python library.