The purpose of this study was to compare the predictive power of fuzzy neural network with fuzzy wavelet neural network in predicting stock prices of banks in Tehran Stock Exchange. The period of this research was from 2011 to 2016. In this research, the fuzzy logic system with the use of a multi-layer neural network system with an error-optimized back-propagation optimization structure and a Maximum Overlapping Discrete Wavelet Transform for exchange rate variables, opec oil, each ounce of gold, the total stock index as well as the volume of trades were used in order to predict stock prices.The results of the model were done by using the updated cost function. The results of the research in comparison of fuzzy wavelet network and fuzzy neural network showed that the reliability of banks with fuzzy wavelet neural network is over 90% and with fuzzy neural network above is 80%. As a result, fuzzy wavelet neural network is more reliable than fuzzy neural network

This paper presents an indirect adaptive type-2 fuzzy neural controller to control a class of nonlinear systems using feedback linearization theory and type-2 fuzzy neural networks. In this method, feedback linearization control input using two type-2 fuzzy neural networks is approximated. The gradient descent learning algorithm with adaptive learning rate is used for learning phase. In this paper, reduction in the number of fuzzy rules is proposed to simplify type-2 fuzzy neural network. By reducing the number of fuzzy rules, model complexity is diminished. Using Lyapunov stability theory, with the expression theorem, adaptation laws for tuning type-2 fuzzy neural networks is derived and stability of the closed loop system is guaranteed. Finally, the proposed controller is applied to twin-tank system in the presence of noisy data. Results are compared with Adaptive-Network-Based Fuzzy Inference System (ANFIS). Simulation results show the ability of the proposed method.

Despite the use of robust statistical methods and fuzzy-neural networks, models that predict the distribution of organisms have seen rapid development in the field of ecology. However, due to the challenges associated with sampling, these studies often lack sufficient samples. In this research, we compared geostatistics and fuzzy-neural networks to estimate the distribution of the tomato fruit worm in a tomato farm in Kermanshah city. For this purpose, the length and width coordinates of the sampling points at the field level were identified and used as inputs for both methods. The output of each method was the count of this pest at those locations. In the geostatistics approach, we employed the normal Kriging method, while in the fuzzy-artificial neural network approach, we used the sigmoid activation function. A comparison of the results from geostatistics and the fuzzy-neural network demonstrated the superior performance of the fuzzy-neural network. The coefficient of determination for the fuzzy-neural network and geostatistics was 0.9 and 0.6, respectively. In conclusion, the fuzzy-neural network method, by integrating latitude and longitude factors, was able to predict the density of the tomato fruit worm with high accuracy.

In this paper, a new hybrid method is presented based on fuzzy neural network for estimating the fuzzy coefficients or parameters of fuzzy supply and demanding the labor function with fuzzy inputs. Here, the neural network is considered as a part of a large field called neural computing or soft computing. Moreover, in order to find the approximate parameters, a simple algorithm is proposed from the cost function of the fuzzy neural network. Finally, we illustrate the approach by the help of some numerical examples.

In this paper, we present a numerical method for solving fully fuzzy polynomials. Theproposed method is based on approximating fuzzy neural network. This method can alsolead to improving numerical methods. In this work, an architecture of fuzzy neural networks is also proposed to find a fuzzy root of a fuzzy polynomial (if exists) by introducing a learning algorithm. We propose a learning algorithm from the cost function for adjusting fuzzy weights. Finally, we illustrate our approach by numerical examples.

Tourism, as a clean and high employment generating industry, has been amongst the world’s top income generating industries and received a great deal of attention during the recent decades by development policies and programs.Needless to say, governments and private sectors in at macro and micro levels need to forecast the demand in tourism to survive and develop. Various quantitative and qualitative methods have been suggested in forecasting tourism demand in the literature so far, most of them focusing on quantitative methods, but with the quite recent advent of artificial neural networks and fuzzy neural networks methods, few studies have employed artificial intelligence methods in tourism demand modeling and forecasting.This study intends to forecast Iran’s inbound tourism demand using a suggestive model of fuzzy neural networks and compare its accuracy with ARIMA method. After identification and prioritization of the most significant determinants of Iran’s tourism demand function, and identification of the architecture for the fuzzy neural networks model, the study concludes that thismethod excels over ARIMA in all theperformance forecasting assessment criteria.

Nowadays, prediction of corporate bankruptcy is one of the most important issues which have received great attentions among academia and practitioners. Although several studies have been accomplished in the field of bankruptcy prediction, less attention has been devoted for proposing a systematic approach based on fuzzy neural networks. The present study proposes fuzzy neural networks to predict bankruptcy of the listed companies in the Tehran stock exchange. Four input variables including growth, profitability, productivity and asset quality were used for prediction purpose. Moreover, the Altman's Zscore is used as the output variable. The results reveal that the proposed fuzzy neural network model has a high performance for the bankruptcy prediction of the companies.

The homogeneous charge compression ignition (HCCI) engines with ethanol fuel as a renewable fuel is a promising solution to some of the major challenges of combustion engines. Incomplete or misfiring combustion limits HCCI operation and damages the catalyst converter and exhaust systems. The experimental data of a 0.3-liter combustion engine was used for modeling and detecting misfiring combustion. Incomplete and misfiring combustion in HCCI engine was studied by fuzzy-neural network. There is a significant relationship between misfiring combustion and in-cylinder pressure variations at 0, 5, 10, 15 and 20 crankshafts. These experimental findings were used to design a fuzzy-neural network for misfiring incomplete combustion in a HCCI engine. This model has been tested on experimental data. The results showed that the fuzzy-neural network fault diagnostic model can detect incomplete and misfiring combustion in HCCI engine with ethanol fuel. In addition, the developed model was able to identify the transition success from the normal operating area and incomplete combustion.

Type-2 fuzzy neural networks have a good ability in identification and control of nonlinear systems, time varying systems and also system with uncertainties. In this paper a new method for designing adaptive inverse type 2 fuzzy neural controllers for online control of nonlinear dynamical system has been introduced. The proposed network has seven layers that the first two layers consist of type-2 fuzzy neurons with uncertainty in mean of Gaussian membership functions, are used for fuzzification part. Third layer is the fuzzy rules layers. Reduction type is done in fourth layer with adaptive nodes. Reminder layers are used for consequent left–right firing points, two end-points and output of network respectively. In this paper, gradient descent with adaptive learning rate backpropagation is used for learning phase. Finally, Type-2 online Sugeno fuzzy neural network is used for tracking control of nonlinear dynamical water bath temperature system. Results are compared with Adaptive-Network-Based Fuzzy Inference System (ANFIS). Simulation results show the proposed method has a good efficiency.

In this disquisition، has been paid to comparing the performance of static and dynamics neural network by purpose choosing appropriate model in predicting of Tehran Stock Exchange. The data used in this study consists of daily and interval of time 1388/1/5 to 1390/8/30، that Including 616 observation for in sample and out of sample forecasting. Approximately 90% of these observations (556 data) use to estimate coefficients of the model and the rest of them (60 data) use to forecast out of sample. Models are also employed in this research; two stationary neural network models such as fuzzy neural network (ANFIS) and artificial neural network (ANN) and a dynamic regression neural network model (NNARX). The results of this survey indicate that According to Criteria to calculate the forecast error، among Mean squared error (MSE) and root mean square error (RMSE)، Fuzzy neural network model of static، dynamic regression models، neural networks، and finally static artificial neural network models have lowest prediction error، Respectively.