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Abstract: In this paper, a recurrent compensatory neuro-fuzzy system (RCNFS) for identification and prediction is proposed. The compensatory-based fuzzy method uses the adaptive fuzzy operations of neuro-fuzzy systems to make fuzzy logic systems more adaptive and effective. A recurrent network is embedded in the RCNFS by adding feedback connections in the second layer, where the feedback units act as memory elements. In this paper, the RCNFS model is proved to be a universal approximator. Also, an online learning algorithm is proposed which can automatically construct the RCNFS. There are no rules initially in the RCNFS. They are created and adapted as online learning proceeds through simultaneous structure and parameter learning. Structure learning is based on the degree measure and parameter learning is based on the ordered derivative algorithm. Finally, the RCNFS is used in several simulations. The simulation results of the dynamic system model have shown that (1) the RCNFS model converges quickly; (2) the RCNFS model requires a small number of tuning parameters; (3) the RCNFS model can solve temporal problems and approximate a dynamic system. [Copyright &y& Elsevier]

In this paper, we propose an Adaptive Neuro-Fuzzy Network (ANFN) to deal with forecasting problems. The ANFN model is inherently a modified Takagi-Sugeno-Kang-type fuzzy-rule-based model possessing a neural network's learning ability. We propose a hybrid learning algorithm which combines the Genetic Algorithm (GA) and the Least-Squares Estimate (LSE) method to construct the ANFN model. The GA is used to tune membership functions at the precondition part of fuzzy rules, while the LSE method is used to tune parameters at the consequent part of fuzzy rules. Simulations demonstrate that the proposed ANFN model has a good predictive capability. [ABSTRACT FROM AUTHOR]

The aim of this paper is to distinguish the vehicle detection and count the class number in each classification from the inputs. We proposed the use of Fuzzy Guided Scale Choice (FGSC)-based SSD deep neural network architecture for vehicle detection and class counting with parameter optimization. The 'FGSC' blocks are integrated into the convolutional layers of the model, which emphasize essential features while ignoring less important ones that are not significant for the operation. We created the passing detection lines and class counting windows and connected them with the proposed FGSC-SSD deep neural network model. The 'FGSC' blocks in the convolution layer emphasize essential features and find out unnecessary features by using the scale choice method at the training stage and eliminate that significant speedup of the model. In addition, FGSC blocks avoided many unusable parameters in the saturation interval and improved the performance efficiency. In addition, the Fuzzy Sigmoid Function (FSF) increases the activation interval through fuzzy logic. While performing operations, the FGSC-SSD model reduces the computational complexity of convolutional layers and their parameters. As a result, the model tested Frames Per Second (FPS) on edge artificial intelligence (AI) and reached a real-time processing speed of 38.4 and an accuracy rate of more than 94%. Therefore, this work might be considered an improvement to the traffic monitoring approach by using edge AI applications. [ABSTRACT FROM AUTHOR]