Neuro fuzzy control of the FES assisted freely swinging leg of paraplegic subjects

The authors designed a neuro fuzzy control strategy for control of cyclical leg movements of paraplegic subjects. The cyclical leg movements were specified by three `swing phase objectives', characteristic of natural human gait. The neuro fuzzy controller is a combination of a fuzzy logic controller and a neural network, which makes the controller self tuning and adaptive. Two experiments have been performed, in which the performance of the neuro fuzzy control strategy has been compared with conventional PID control strateg

A Neuro-Fuzzy Approach in the Prediction of Financial Stability and Distress Periods

The purpose of this paper is to present a neuro-fuzzy approach of financial distress pre-warning model appropriate for risk supervisors, investors and policy makers. We examine a sample of the financial institutions and electronic companies of Taiwan Security Exchange (TSE) from 2002 through 2008. We present an adaptive neuro-fuzzy system with triangle and Gaussian membership functions. We conclude that neuro-fuzzy model presents almost perfect forecasts for financial distress periods as also very high forecasting performance for financial stability periods, indicating that ANFIS technology is more appropriate for financial credit risk control and management and for the forecasting of bankruptcy and distress periods. On the other hand we propose the use of both models, because with Logit and generally with discrete choice models we can examine and investigate the effects of the inputs or the independent variables, while we can simultaneously use ANFIS for forecasting purposes. The wise and the most scientific option are to combine both models and not taking only one of themFinancial distress; ANFIS; Neuro-Fuzzy; Fuzzy rules; Fuzzy membership functions; triangle; Gaussian; MALTAB

An ANFIS Control Approach of a Bi-Directional Buck-Boost used for a Battery Charger

In this work, a neuro-fuzzy regulator based on ANFIS is designed for the current control of a Bidirectional Buck-Boost chopper battery charger. First, a PI regulator is used in the control loop. Data from the PI regulator is extracted and then used to train ANFIS. The performances of the two PI and neuro-fuzzy commands were evaluated under the MATLAB/SIMULINK environment. According to the simulation results, it was found that the neuro-fuzzy regulator ANFIS is more effective in improving the current response by reducing the response time. In conclusion, the neuro-fuzzy control gives a better performance compared to the PI control

ANFIS optimized semi-active fuzzy logic controller for magnetorheological dampers

In this paper, we report on the development of a neuro-fuzzy controller for magnetorheological dampers using an Adaptive Neuro-Fuzzy Inference System or ANFIS. Fuzzy logic based controllers are capable to deal with non-linear or uncertain systems, which make them particularly well suited for civil engineering applications. The main objective is to develop a semi-active control system with a MR damper to reduce the response of a three degrees-of-freedom (DOFs) building structure. The control system is designed using ANFIS to optimize the fuzzy inference rule of a simple fuzzy logic controller. The results show that the proposed semi-active neuro-fuzzy based controller is effective in reducing the response of structural system.info:eu-repo/semantics/publishedVersio

Load Frequency Control in Two Area Power System using ANFIS

The load-frequency control (LFC) is used to restore the balance between load and generation in each control area by means of speed control. The main goal of LFC is to minimize the transient deviations and steady state error to zero in advance. This paper investigated LFC using proportional integral (PI) Controller and Adaptive Neuro Fuzzy Inference System (ANFIS) for two area system. The results of the two controllers are compared using MATLAB/Simulink software package. Comparison results of conventional PI controller and Adaptive Neuro Fuzzy inference System are presented. Keywords: Load Frequency Control, Adaptive Neuro Fuzzy Inference System (ANFIS), PI controller

Design of bang-bang controller based on a fuzzy-neuro approach

A fuzzy-neuro approach for the design of bang-bang controller is presented in this paper. The approach has been used with success for the time optimal bang-bang control of a heating system. The improved bang-bang controller suppresses the oscillations often observed at the output of an on-off controller. A fuzzy system is used for the implementation of the on-off control. An extension of the fuzzy control is provided by an equivalent neural network of the fuzzy system. A test application, that of a house heating with a two-state furnace, is developed and evaluated with standard hysteresis switching, fuzzy control, and fuzzy-neuro control.published_or_final_versio

COMPARATIVE ANALYSIS OF NEURO- FUZZY AND SIMPLEX OPTIMIZATION MODEL FOR CONGESTION CONTROL IN ATM NETWORK.

Congestion always occurred when the transmission rate increased the data handling capacity of the network. Congestion normally arises when the network resources are not managed efficiently. Therefore if the source delivers at a speed higher then service rate queue, the queue size will be higher. Also if the queue size is finite, then the packet will observed delay. MATLAB Software was used to carry out simulations to develop Congestion control optimization Scheme for ATM Network with the aims to reducing the congestion of Enugu ATM Network. The results of the research reveal the minimization of congestion application model for Enugu ATM using optimization and Neuro-fuzzy. The result shows that congestion control model with Optimization and Neuro-fuzzy were 0.00003153 and 0.00002098 respectively. The ATM Congestion was reduced by 0.0000105, which is 18.2% decrease after Neuro-fuzzy controller was used. The results show the application of Neuro-fuzzy model which can use to control and minimized the ATM Congestion of Enugu ATM Network. The result shows that when Neuro-fuzzy is applied the congestion and the packet queue length in the buffer will be minimized. Key words: Congestion, MATLAB, Optimization, Neuro-fuzzy, ATM DOI: 10.7176/CTI/10-05 Publication date:July 31st 2020

Aplikasi Neuro Fuzzy Controller Pada Sistem Titrasi Pengolah Limbah Cair

This research is aimed at planning and measuriang the system of liquid waste processing devide with ply neutral reaction that is controlled by computer based on neuro fuzzy controller, in which the system control is fuzzy logical system than can improve control out put response based on nervous net imitation. In this system, it can be seen that computer has a very important role that is to control the proless of all activities in waste processing. Key ward. PH, Neuro fuzzy controlle