Green hydrogen is considered to be one of the best candidates for fossil fuels in near future. Bio-hydrogen production from dark fermentation organic materials, including wastes, most cost-effective and promising methods production. One main challenges posed by this method low rate. Therefore, optimizing operating parameters, such as initial pH value, temperature, N/C ratio, concentration (xylo...

This work is an attempt to illustrate the usage and effectiveness of soft computing techniques in the estimation and control of multi input and multi output systems. This paper focuses on neuro-fuzzy system ANFIS (Adaptive Neuro Fuzzy Inference system). An Adaptive Network based Fuzzy Interference System architecture extended to cope with multivariable systems has been used. The performance of ...

This paper presents a new general purpose neuro-fuzzy controller to realize adaptive-network-based fuzzy inference system (ANFIS) architecture. ANFIS which tunes the fuzzy inference system with a back propagation algorithm based on collection of input-output data makes fuzzy system to learn. To implementing this idea we propose several improved CMOS analog circuits, including Gaussian-like memb...

Fuzzy adaptive network (FAN) is proposed to help decision makers in credit scores and to assign the amount of loan. By combining with neural networks to incorporate the learning ability, FAN provides an alternative approach for the imprecision and fuzziness of the credit rating system. A loan approval example is given and the performance of FAN is compared with the regression algorithm. The res...

Navigation and obstacle avoidance in an unknown environment is proposed in this paper using hybrid neural network with fuzzy logic controller. The overall system is termed as Adaptive Neuro Fuzzy Inference System (ANFIS). ANFIS combines the benefits of fuzzy logic and neural networks for the purpose of achieving robotic navigation task. Simulation results are presented using Khepera Simulator (...

In this paper an adaptive neuro fuzzy inference system based on interval Gaussian type-2 fuzzy sets in the antecedent part and Gaussian type-1 fuzzy sets as coefficients of linear combination of input variables in the consequent part is presented. The capability of the proposed method (we named ANFIS2) to function approximation and dynamical system identification is shown. The ANFIS2 structure ...