Fuzzy systems represent the promising new generation of information processing systems. An adaptive neuro-fuzzy inference system or adaptive network-based fuzzy inference system (ANFIS) is a kind of artificial neural network that is based on Takagi–Sugeno fuzzy inference system. The technique was developed in the early 1990s. Since it integrates both neural networks and fuzzy logic principles, it has potential to capture the benefits of both in a single framework. Its inference system corresponds to a set of fuzzy IF–THEN rules that have learning capability to approximate nonlinear functions. Hence, ANFIS is considered to be a universal estimator. For using the ANFIS in a more efficient and optimal way, one can use the best parameters obtained by genetic algorithm. Adaptive network based fuzzy inference systems are good at tasks such as pattern matching and classification, function approximation, optimization and data clustering, while traditional computers, because of their architecture, are inefficient at these tasks, especially pattern-matching tasks. In the proposed special issue, we want to focus on application of adaptive network based fuzzy inference systems in power networks. For this purpose, the adaptive network based fuzzy inference systems may be applied to DC motor speed control, FACTS devices control and so on.

Aims and Scope:

1. Motor Speed Control
2. FACTS devices
3. Transformers
4. Voltage Profile
5. Optimization