The paper presents the neuro-fuzzy controller algorithm for air conditioning system. Neuro-fuzzy control combines the learning capabilities of neural networks and control capabilities of fuzzy logic control. The neurofuzzy controller for air conditioning system takes two inputs from temperature and humidity sensors and controls the compressor speed. The experimental results of the developed sys...

Cancer research is one of the major research areas in the medical field. Adaptive Neuro Fuzzy Interference System is used for the classification of Cancer. This algorithm compared with proposed algorithm of Adaptive Neuro Fuzzy Interference system with Runge Kutta learning method for the best classification of cancer. It is one of the better techniques for the classification of the cancer. The ...

A fuzzy logic controller (FLC) is designed to maintain constant tension for tandem rolling mills. Envisioning fuzzy inference system as neural network and introducing tutor, backward propagation algorithm is used as self-organization technique for FLC to approach the best parameters under supervision. Simulation results exhibit the generalization and adaptivity of neuro-fuzzy controller in offl...

The paper describes a Neuro-fuzzy approach with additional moving average window data filter and fuzzy clustering algorithm that can be used to forecast electrical load using the Takagi-Sugeno (TS) type multi-input single-output (MISO) neurofuzzy network efficiently. The training algorithm is efficient in the sense that it can bring the performance index of the network, such as the sum squared ...

The architecture of forecasting adaptive wavelet-neuro-fuzzy-network and its learning algorithm for the solving of nonstationary processes forecasting tasks are proposed. The learning algorithm is optimal on rate of convergence and allows to tune both the synaptic weights and dilations and translations parameters of wavelet activation functions. The simulation of developed wavelet-neuro-fuzzy n...

The architecture of adaptive wavelet-neuro-fuzzy-network and its learning algorithm for the solving of nonstationary processes forecasting and emulation tasks are proposed. The learning algorithm is optimal on rate of convergence and allows tuning both the synaptic weights and dilations and translations parameters of wavelet activation functions. The simulation of developed wavelet-neuro-fuzzy ...

To accurately estimate the state of charge of a lithium-ion battery pack used in electric vehicles, a neurofuzzy system is proposed. The subtractive clustering is used to determine the structure and the initial parameters of the neuro-fuzzy system to reduce heuristic errors. The algorithm of adaptive neuro-fuzzy inference (ANFIS) is adopted to optimize the parameters of the neuro-fuzzy system. ...

This paper proposes the application of Genetic Learning as a procedure for the optimal design and training of neuro-fuzzy systems. Once this learning procedure has been implemented, hybridization between Genetic Algorithms (GA) and the traditional local search technique is carried out to form a Hybrid Algorithm, in order to achieve the maximum possible efficiency in the search, and to be able t...

the oxidative coupling of methane (ocm) performance over na-w-mn/sio2 at elevated pressures has been simulated by adaptive neuro fuzzy inference system (anfis) using reaction data gathered in an isothermal fixed bed microreactor. in the designed neuro fuzzy models, three important parameters such as methane to oxygen ratio, gas hourly space velocity (ghsv), and reaction temperature were conside...

Real life data sets often suffer from missing data. The neuro-rough-fuzzy systems proposed hitherto often cannot handle such situations. The paper presents a neuro-fuzzy system for data sets with missing values. The proposed solution is a complete neuro-fuzzy system. The system creates a rough fuzzy model from presented data (both full and with missing values) and is able to elaborate the answe...