Correction: Pourdaryaei et al. Recent Development in Electricity Price Forecasting Based on Computational Intelligence Techniques in Deregulated Power Market. Energies 2021, 14, 6104

There was an error in the original publication [1]. Some phrases in the original publication were not appropriate. The authors would like to change (enrollment capacities) to (activation functions); (enrollment works) to (membership function); and (fluffy) to (fuzzy).

A correction has been made to Section 3.2, Paragraph Numbers 6 & 9:

3.2. 

Adaptive Neuro-Fuzzy Inference System (ANFIS)

In numerous regards, the Mamdani-based FIS approach is similar to the Sugeno approach. A comparative fuzzy deduction is prepared for both approaches by the implementation of the fuzzifying process upon the input information and the fuzzy administrators. The foremost distinction between Sugeno-based FIS and Mamdani-based FIS is that the manner of the fuzzy inputs has changed over to a fresh yield. In Mamdani-based FIS, when computing the fresh yield, the fuzzy yield is employed in the defuzzification strategy, whereas in Sugeno-based FIS, the weighted normal strategy is utilized. The idea of disposing of Mamdani interpretability and expressive control is the aim of the strategies because the standard consequence of the Sugeno strategy is not fuzzy. Sugeno has a quicker interim time compared to Mamdani-based FIS; rather than the time-devouring defuzzification, it prepares the connection to the weighted normal strategy. Due to the instinctive nature of and operation of this view, it has led to the complex strategy of Mamdani, with the choice-back application thought to be linked. Additionally, Sugeno and Mamdani-based FISs show more contrast between them due to the fact that Sugeno has no yield participation capacities compared to Mamdani FIS yield participation; therefore, the Sugeno strategy gives a yield that is either a direct (weighted) numerical expression or that is steady. The Mamdani strategy provides a yield that is a fuzzy set. Sugeno has more adaptability in the framework plan than Mamdani-based FIS, as demonstrated by the more efficient frameworks that can be achieved if the ANFIS device is coordinated with [48].

Layer 1: In this layer, the inputs are x and y to hub i, their etymological names are Ai and Bi, the activation functions for Ai and Bi fuzzy sets are μAi and μBi, separately, and the enrollment review of a fuzzy set is known as q1. i is regarded as the yield of hub i within a layer that indicates the degree to which the specified input (x or y) fulfills the evaluation. In ANFIS, the MF (membership function) for a fuzzy set can regularly be any parameterized participation work, such as universal Chime molded work, Gaussian, trapezoidal, or triangular.

The authors state that the scientific conclusions are unaffected. The correction was approved by the Academic Editor. The original publication has also been updated.