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Statistical Analysis and Design of Chaotic Systems
Published in M.P. Kennedy, R. Rovatti, G. Setti, Chaotic Electronics in Telecommunications, 2018
Wolfgang Schwarz, Marco Götz, Kristina Kelber, Andreas Abel, Thomas Falk, Frank Dachselt
Example: Tent map. The tent map is piecewise linear and Markovian. Its Markov partition is ΠM = {IM1 = [–1, 0), IM2 = [0, 1]} and we have XM1 = XM2 = IM1 ∪ IM2. The map segments are () φ1(z)=2⋅z+1,z∈IM1φ2(z)=−2⋅z+1.z∈IM2
Forecasting the PV Power Utilizing a Combined Convolutional Neural Network and Long Short-Term Memory Model
Published in Electric Power Components and Systems, 2023
Ramakrishnan Raman, Bhaveshkumar Mewada, R. Meenakshi, G. M. Jayaseelan, K. Soni Sharmila, Syed Noeman Taqui, Essam A. Al-Ammar, Saikh Mohammad Wabaidur, Amjad Iqbal
The hybrid network model’s forecast performance is noticeably higher than that of a single network model, demonstrating that it is equally as capable as LSTM when dealing with sequences with clear rules, but LSTM has superior learning capabilities when handling complex jobs. It illustrates the potent ability of SSA training parameters, which not only prevents over-fitting of the model training but also prevents the gradient from evaporating or bursting during the training, boosting forecast reliability when compared to the optimized hybrid network model. As a result, the analysis of several error indicators illustrate that the ABC-CLSTM combinational network model works well. As a result of the development of the Tent chaotic map, it is essential to take notice that the population initialization is now more rational, and the accuracy of the approximation solution has been improved. All of these factors have an effect on the pace at which the solution converges. The tent map exhibits a wide variety of dynamic response, ranging from foreseeable to chaotic, according to the value of μ. In addition to converging more quickly, the optimized hybrid network model also marginally increases prediction accuracy. Even though the speed of convergence is faster, the better chaotic mapping’s influence makes it difficult to enter the local ideal condition. The upgraded ABC will exhibit great efficiency in the prediction outcomes of numerous comparative models exactly since it offers the aforementioned benefits. Figure 9 shows the Error accuracy for extreme weather conditions. Table 3 shows the Errors prediction models for extreme weather.
Optimizing facility layout planning for reconfigurable manufacturing system based on chaos genetic algorithm
Published in Production & Manufacturing Research, 2019
Xiaoxiao Wei, Sicong Yuan, Yuanqin Ye
In the Logistic map space [0, 1], there are 0.25, 0.5 and 0.75 discontinuous points. The uneven distribution of the mapping points which is ‘high sides and low middle’ will directly affect the convergence speed of the whole iteration and reduce the efficiency of the algorithm. Tent mapping, also known as tent map, is a one-dimensional mapping method that is sensitive to initial values. The mapping equation is: