## 1 Introduction
Process control uses artificial neural networks because they can develop prognostic models of the process from multidimensional data that are routinely collected from sensors [12]. An ANN model comprises many nodes and their connections. Structure, transfer function, and learning algorithms define its capacity [13]. ANNs are not programmed to perform specific tasks when applied to computers. Instead, they are trained on datasets until they learn the patterns available to them. They can be trained to generate quick predictions and generalizations; are fault-tolerant in that they can deal with noisy and imperfect data; and can learn from examples. Once trained, new models can be presented to them for prediction or classification. ANNs have been used in a variety of applications in control, robotics, pattern recognition, prediction, medicine, power systems, manufacturing, optimization, signal processing, and social and psychological sciences [12]. The study of thinking systems in which the ideas of truth and falsity are handled gradually is known as fuzzy logic. Fuzzy logic examines ambiguity in natural language as well as in a number of other application domains. In essence, fuzzy logic makes it possible to characterize values that fall between assessments like high-low, yes-no, and true-false [14]. A mathematical theory called fuzzy logic uses verbal explanations to deal with datasets whose meaning does not directly correspond to a single numerical value but rather to a range of potential values. On these datasets, domain-specific functions can be designed to produce results with a sufficient degree of approximation that are relevant to the receiver. The meaning of the field-specific values of the dataset can be explained using fuzzy terms that have ambiguous definitions at their borders thanks to input membership functions. Fuzzy logic systems are knowledge- or rule-based systems built from human knowledge in the form of fuzzy IF-THEN rules. Fuzzy system theory, a systematic procedure, enables the use of the knowledge-base to transform it into nonlinear mapping. The fuzzy IF-THEN rule is an IF-THEN statement in which some words are characterized by continuous membership functions [15]. The fuzzy logic controller has five basic elements such as blurring, knowledge base, rule base, fuzzy inference, and defuzzification [16]. These steps help us define a logical framework based on linguistic values that are defined and characterized using fuzzy sets. The adaptive neuro-fuzzy inference system (ANFIS) combines the self-learning capacity of an artificial neural network (ANN) with the language expression function of fuzzy inference. Sugeno operates in a fuzzy inference system with a structure similar to a multilayer feedforward neural network. The first fuzzy rules and membership functions are chosen based on the human knowledge of the outputs to be mimicked. ANFIS offers a variety of benefits, such as the capacity to depict the nonlinear nature of a process, adaptability, and quick learning capabilities. For the supplied input-output matches, ANFIS creates a collection of fuzzy IF-THEN rules with the relevant membership functions. A significant amount of the accessible data have been used to learn membership functions and fuzzy rules. Then, the ANFIS can alter these fuzzy IF-THEN rules and membership functions to lessen the output inaccuracy or clarify the relationship between an intricate system's inputs and outputs [17; 18]. Applications of artificial neural networks include wind energy systems for forecasting, defect diagnosis and detection, design optimization, and control optimization. Although predicting wind power generation is a difficult undertaking, it is crucial that energy providers, participants in the wind energy market, owners and operators of wind farms, maintenance crews, etc., construct the best plans possible. The primary factors that must be predicted are wind power and speed [19].