scholarly journals Dynamic System Identification and Prediction Using a Self-Evolving Takagi–Sugeno–Kang-Type Fuzzy CMAC Network

Electronics ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 631
Author(s):  
Cheng-Jian Lin ◽  
Cheng-Hsien Lin ◽  
Jyun-Yu Jhang
Keyword(s):  
Structure Learning ◽  
Gaussian Function ◽  
Cerebellar Model Articulation Controller ◽  
Fuzzy Cmac ◽  
Proposed Model ◽  
Firing Strength ◽  
Gradient Descent Algorithm ◽  
Combination Function ◽  
Takagi Sugeno ◽  
Prediction Problems

This study proposes a Self-evolving Takagi-Sugeno-Kang-type Fuzzy Cerebellar Model Articulation Controller (STFCMAC) for solving identification and prediction problems. The proposed STFCMAC model uses the hypercube firing strength for generating external loops and internal feedback. A differentiable Gaussian function is used in the fuzzy hypercube cell of the proposed model, and a linear combination function of the model inputs is used as the output of the proposed model. The learning process of the STFCMAC is initiated using an empty hypercube base. Fuzzy hypercube cells are generated through structure learning, and the related parameters are adjusted by a gradient descent algorithm. The proposed STFCMAC network has some advantages that are summarized as follows: (1) the model automatically selects the parameters of the memory structure, (2) it requires few fuzzy hypercube cells, and (3) it performs identification and prediction adaptively and effectively.

A Novel Interactively Recurrent Self-Evolving Fuzzy CMAC and Its Classification Applications

2015 ◽  
Vol 14 (03) ◽  
pp. 1550019
Author(s):  
Jyun-Guo Wang ◽  
Shen-Chuan Tai ◽  
Cheng-Jian Lin
Keyword(s):  
Structure Learning ◽  
Learning Algorithm ◽  
Descent Method ◽  
Superior Performance ◽  
Gradient Descent Method ◽  
Parameter Learning ◽  
Cerebellar Model Articulation Controller ◽  
Classification Problems ◽  
Fuzzy Cmac ◽  
Firing Strength

In this paper, an Interactively Recurrent Self-evolving Fuzzy Cerebellar Model Articulation Controller (IRSFCMAC) model is developed for solving classification problems. The proposed IRSFCMAC classifier consists of internal feedback and external loops, which are generated by the hypercube cell firing strength to itself and other hypercube cells. The learning process of the IRSFCMAC gets started with an empty hypercube base, and then all of hypercube cells are generated and learned online via structure and parameter learning, respectively. The structure learning algorithm is based on the degree measure to determine the number of hypercube cells. The parameter learning algorithm, based on the gradient descent method, adjusts the shapes of the membership functions and the corresponding fuzzy weights of the IRSFCMAC. Finally, the proposed IRSFCMAC model is tested by four benchmark classification problems. Experimental results show that the proposed IRSFCMAC model has superior performance than traditional FCMAC and other models.

scholarly journals Design of Adaptive TSK Fuzzy Self-Organizing Recurrent Cerebellar Model Articulation Controller for Chaotic Systems Control

2021 ◽  
Vol 11 (4) ◽  
pp. 1567
Author(s):  
Shun-Yuan Wang ◽  
Chuan-Min Lin ◽  
Chen-Hao Li
Keyword(s):  
Chaotic Systems ◽  
Tracking Error ◽  
Extensive Study ◽  
Fuzzy Rules ◽  
Cerebellar Model Articulation Controller ◽  
Control Performance ◽  
Fuzzy Cmac ◽  
Structure Complexity ◽  
Takagi Sugeno ◽  
Self Organizing

The synchronization and control of chaos have been under extensive study by researchers in recent years. In this study, an adaptive Takagi–Sugeno–Kang (TSK) fuzzy self-organizing recurrent cerebellar model articulation controller (ATFSORC) is proposed, which is composed of a set of TSK fuzzy rules, a cerebellar model articulation controller (CMAC), a recurrent CMAC (RCMAC), a self-organizing CMAC (SOCMAC), and a compensation controller. Specifically, SOCMAC, RCMAC, and adaptive laws are adopted so that the association memory layers of ATFSORC can be modulated in accordance with the layer decision-making mechanism in order to reduce the structure complexity and improve the control performance of ATFSORC. Moreover, the Takagi–Sugeno–Kang fuzzy rules are introduced to increase the learning speed of ATFSORC, and the improved compensating controller is designed to dispel the errors between an ideal controller and the TFSORC. Moreover, the proposed ATFSORC is applied to chaotic systems in order to validate its performance and feasibility. Several simulation schemes are demonstrated to show the effectiveness of the proposed method. Simulation results show that the proposed ATFSORC can obtain a favorable control performance when the chaotic systems are operated at different parameters. Specifically, ATFSORC can achieve faster convergence of the tracking error than fuzzy CMAC (FCMAC) and CMAC.

scholarly journals Short-Term Prediction of Bus Passenger Flow Based on a Hybrid Optimized LSTM Network

2019 ◽  
Vol 8 (9) ◽  
pp. 366 ◽  
Author(s):  
Yong Han ◽  
Cheng Wang ◽  
Yibin Ren ◽  
Shukang Wang ◽  
Huangcheng Zheng ◽  
...  
Keyword(s):  
High Efficiency ◽  
Short Term Memory ◽  
Spatial Scales ◽  
Optimization Procedure ◽  
Stochastic Gradient Descent ◽  
Short Term ◽  
Passenger Flow ◽  
Proposed Model ◽  
Gradient Descent Algorithm ◽  
Lstm Network

The accurate prediction of bus passenger flow is the key to public transport management and the smart city. A long short-term memory network, a deep learning method for modeling sequences, is an efficient way to capture the time dependency of passenger flow. In recent years, an increasing number of researchers have sought to apply the LSTM model to passenger flow prediction. However, few of them pay attention to the optimization procedure during model training. In this article, we propose a hybrid, optimized LSTM network based on Nesterov accelerated adaptive moment estimation (Nadam) and the stochastic gradient descent algorithm (SGD). This method trains the model with high efficiency and accuracy, solving the problems of inefficient training and misconvergence that exist in complex models. We employ a hybrid optimized LSTM network to predict the actual passenger flow in Qingdao, China and compare the prediction results with those obtained by non-hybrid LSTM models and conventional methods. In particular, the proposed model brings about a 4%–20% extra performance improvements compared with those of non-hybrid LSTM models. We have also tried combinations of other optimization algorithms and applications in different models, finding that optimizing LSTM by switching Nadam to SGD is the best choice. The sensitivity of the model to its parameters is also explored, which provides guidance for applying this model to bus passenger flow data modelling. The good performance of the proposed model in different temporal and spatial scales shows that it is more robust and effective, which can provide insightful support and guidance for dynamic bus scheduling and regional coordination scheduling.

scholarly journals An Efficient Approach for Segmentation and Classification of Acute Lymphoblastic Leukemia via Optimized Spike-based Network

2021 ◽  
Author(s):  
Ali Noshad ◽  
saeed fallahi
Keyword(s):  
Acute Lymphoblastic Leukemia ◽  
Blood Cells ◽  
Lymphoblastic Leukemia ◽  
Gaussian Function ◽  
Principal Component ◽  
Complex Issue ◽  
Morphological Variations ◽  
Learning Techniques ◽  
Proposed Model

Abstract Identification of uncontrolled accumulation of abnormal blood cells ( lymphoblasts ) considered to be a challenging task. Despite a wide variety of image processing and deep learning techniques, the task of extracting the features from Acute Lymphoblastic Leukemia (ALL) images and detection of ALL cells is still challenging and complex issue due to morphological variations in cells. In order to overcome these drawbacks, in this study, we proposed a new framework with a combination of spiking and residual network for the detection and classification of lymphoblasts cells from healthy ones in blood sample images. According to this, features are extracted using a novel First-Spike-based approach, and then the Gaussian function is applied to remove the low-intensity edges. To reduce dimensionality, Principal Component Analysis ( PCA ) is used and finally, a developed deep residual architecture is employed to diagnose the ALL blood cells from the reconstructed images. To show the effectiveness of the proposed model, it is evaluated on microscopic images of blood samples from ALL Images (ALL- IDB ) and ISBI -2019 C- NMC dataset. The results show the superiority of the model to be an appropriate choice for future biomedical imaging tasks.

scholarly journals A Distributed Adaptive Neuro-Fuzzy Network for Chaotic Time Series Prediction

2015 ◽  
Vol 15 (1) ◽  
pp. 24-33 ◽  
Author(s):  
Margarita Terziyska
Keyword(s):  
Time Series Prediction ◽  
Model Performance ◽  
Inference Mechanism ◽  
Neuro Fuzzy ◽  
Learning Procedure ◽  
Gradient Descent Algorithm ◽  
Chaotic Time Series Prediction ◽  
Takagi Sugeno ◽  
Fuzzy Network ◽  
Time Systems

Abstract In this paper a Distributed Adaptive Neuro-Fuzzy Architecture (DANFA) model with a second order Takagi-Sugeno inference mechanism is presented. The proposed approach is based on the simple idea to reduce the number of the fuzzy rules and the computational load, when modeling nonlinear systems. As a learning procedure for the designed structure a two-step gradient descent algorithm with a fixed learning rate is used. To demonstrate the potentials of the selected approach, simulation experiments with two benchmark chaotic time systems − Mackey-Glass and Rossler are studied. The results obtained show an accurate model performance with a minimal prediction error.

A Homotopic Algorithm of NN and its Application for the End-Point Prediction Model of BOF Process

2014 ◽  
Vol 513-517 ◽  
pp. 431-434
Author(s):  
Ming Xia Feng ◽  
Ren Chen ◽  
Qiang Li
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Homotopy Theory ◽  
Smelting Process ◽  
Original Result ◽  
New Model ◽  
End Point ◽  
Proposed Model ◽  
Gradient Descent Algorithm

A Homotopic BI neural network model is developed by combining the homotopy theory and the BI neural network model, to improve the defects of the steepest gradient descent algorithm itself, such as low speed converging and liable to be trapped in local minimum. The end-point carbon content and temperature of molten steel in BOF smelting process is predicted by the proposed model and the original. Result shows that the precision of new model is improved significantly. The hit rates are increased by about 5% and 10%, and the forecasting residuals have decreased 16.31% and 8.67% than the conventional ones, respectively. Also, the calculation time of the new model is 10% shorter than BI model.

scholarly journals Direct Fuzzy CMAC Sliding Mode Trajectory Tracking for Biaxial Position System

Energies ◽  
2021 ◽  
Vol 14 (22) ◽  
pp. 7802
Author(s):  
Wei-Lung Mao ◽  
Yu-Ying Chiu ◽  
Bing-Hong Lin ◽  
Wei-Cheng Sun ◽  
Jian-Fu Tang
Keyword(s):  
Sliding Mode ◽  
Cross Coupling ◽  
Cerebellar Model Articulation Controller ◽  
Model Uncertainties ◽  
Precise Control ◽  
Motion System ◽  
Fuzzy Cmac ◽  
Adaptive Law ◽  
On Line ◽  
Stage System

High-precision trajectory control is considered as an important factor in the performance of industrial two-axis contour motion systems. This research presents an adaptive direct fuzzy cerebellar model articulation controller (CMAC) sliding mode control (DFCMACSMC) for the precise control of the industrial XY-axis motion system. The FCMAC was utilized to approximate an ideal controller, and the weights of FCMAC were on-line tuned by the derived adaptive law based on the Lyapunov criterion. With this derivation in mind, the asymptotic stability of the developed motion system could be guaranteed. The two-axis stage system was experimentally investigated using four contours, namely, circle, bowknot, heart, and star reference contours. The experimental results indicate that the proposed DFCMACSMC method achieved the improved tracking capability, and so reveal that the DFCMACSMC scheme outperformed other schemes of the model uncertainties and cross-coupling interference.

scholarly journals Robust Adaptive Self-Organizing Wavelet Fuzzy CMAC Tracking Control for De-icing Robot Manipulator

2015 ◽  
Vol 10 (4) ◽  
pp. 567 ◽  
Author(s):  
ThanhQuyen Ngo ◽  
TaVan Phuong
Keyword(s):  
Robot Manipulator ◽  
Learning Ability ◽  
Cerebellar Model Articulation Controller ◽  
Robust Controller ◽  
Fast Learning ◽  
Self Organized ◽  
Fuzzy Cmac ◽  
Robust Adaptive ◽  
The Stability ◽  
Self Organizing

In this paper, a robust adaptive self-organizing control system based on a novel wavelet fuzzy cerebellar model articulation controller (WFCMAC) is developed for an n-link robot manipulator to achieve the high-precision position tracking. This proposed controller consists of two parts: one is the WFCMAC approach which is implemented to cope with nonlinearities, due to the novel WFCMAC not only incorporates the wavelet decomposition property with fuzzy CMAC fast learning ability but also it will be self-organized; that is, the layers of WFCMAC will grow or prune systematically. Therefore, dimension of WFCMAC can be simplified. The second is the order which is the adaptive robust controller which is designed to achieve robust tracking performance of the system. The adaptive tuning laws of WFCMAC parameters and error estimation of adaptive robust controller are derived through the Lyapunov function so that the stability of the system can be guaranteed. Finally, the simulation and experimental results of novel three-link deicing robot manipulator are applied to verify the effectiveness of the proposed control methodology.

Transport model for disordered organic nanocomposites

2014 ◽  
Vol 1660 ◽  
Author(s):  
Andrés Vercik
Keyword(s):  
Transport Model ◽  
Gaussian Function ◽  
Electric Transport ◽  
Gold And Silver Nanoparticles ◽  
Trap Charge ◽  
Voltage Range ◽  
Limited Current ◽  
Proposed Model ◽  
Current Voltage ◽  
Low Energies

ABSTRACTElectric transport in disordered media is usually explained in terms of different transport regimes, such as SCLC (Space Charge Limited Current) or TCLC (Trap Charge Limited Current) regimes. These models lead to exponential dependencies of the current on voltage, e.g., quadratic for SCLC or higher order for TCLC, with transition regions between them where fitting is poor. Alternatively, a statistical distribution in space and energy of the disordered traps, e.g., Gaussian or exponential, allows explaining transport in disordered materials. In this work, we propose a modeling based on the density of states (DOS) function, fitted from normalized differential conductivity curves obtained from experimental current-voltage curves. In general a Gaussian function is used for low energies whereas one or more exponential functions are used for higher energies. The proposed model is used to reproduce experimental current-voltage curves of organic nanocomposites, with gold and silver nanoparticles within chitosan matrixes. A unique expression is obtained for a very accurate fitting the experimental current-voltage characteristics in the whole voltage range without transition regions.

Probabilistic evaluation of the area of coverage of a probe used for eddy current non-destructive inspections

2020 ◽  
Vol 64 (1-4) ◽  
pp. 11-18
Author(s):  
Noritaka Yusa ◽  
Takuma Tomizawa ◽  
Haicheng Song
Keyword(s):  
Eddy Current ◽  
Gaussian Function ◽  
Probability Of Detection ◽  
Steel Plates ◽  
Detection Model ◽  
Probabilistic Evaluation ◽  
Proposed Model ◽  
Scanning Line ◽  
Non Destructive ◽  
Type 316L Stainless Steel

This study proposes a method to probabilistically evaluate the area of coverage of nondestructive inspections to detect defects on a surface of a structure. For the specific problem, this study considers the effect of the distance between two neighboring scanning lines on the detectability of eddy current testing against near-side cracks. Thirty-eight type 316L stainless steel plates having a fatigue crack were prepared, and eddy current examinations were performed with a sufficiently fine scanning pitch. The full width at half maximum of the spatial distribution of the amplitude of the signals was approximated using a Gaussian function. A probability of detection model considering the distance between two neighboring scanning lines is proposed because in actual inspections a scanning line does not always run directly above a crack. The results demonstrated that the proposed model enables a reasonable probabilistic evaluation of the effect of the distance between two neighboring scanning lines.

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