fuzzy inference systems

Nowadays, the threats caused by aerial targets are among the most critical challenges. Two main components in threat management are visualization and decision-making based on compiled pictures. In this process, the decisions are made based on threat evaluation and resource allocation modelling. To do so, the sequential nature of resource allocation is considered to provide a more accurately visualized situation. Here, a multi-stage multi-objective model is constructed based on the fuzzy inference system and the decision tree. Also, NSGA-II and SPEA-II are used to find Pareto solutions. Both Algorithms have been compared based on generational distance as a convergence measure, spread as a diversity measure, and the actual computational time. Ultimately, the TOPSIS method is used to make the final decision while the results are reported using a simulated scenario. The simulation results show that the SPEA-II has better convergence and spread, while NSGA-II is faster and has less standard deviation in the execution time. We believe that the fuzzy inference system is more suitable than the decision tree in practical applications. Nevertheless, when facing data shortage or incompatibility, the decision tree would be the preferred choice.

Investing on the stock exchange, as one of the financial resources, has always been a favorite among many investors. Today, one of the areas, where the prediction is its particular importance issue, is financial area, especially stock exchanges. The main objective of the markets is the future trend prices prediction in order to adopt a suitable strategy for buying or selling. In general, an investor should be predicted the future status of the time, the amount and location of his assets in a way that increases the return on his assets. Stock price prediction is one of the most challenging topics in the field of forecasting, which has attracted many attentions from researchers. The various factors of the markets have caused the situation that they always have a dynamic and complex process. Therefore, researchers have been determined to look for new prediction methods of stock price, which will reduce the instability and complexity of the markets. In fact, the most of recent studies have shown that the stock market is a nonlinear, dynamic, and non-parametric system that is affected by various economic factors. The applications of artificial intelligence and machine learning techniques to identify the relationship between the factors and stock price exchanges can be organized in seven major groups such as neural networks and deep learning, support vector machine, decision tree and random forest, k nearest neighbor, regression, Bayesian networks and fuzzy inference-base methods. Due to the mentioned prediction methods have their own challenges, hydridizations of the meta-heuristic algorithms and the methods were applied to stock price prediction. In this paper, a new hybridization of Fuzzy Inference System and a novel modified Fuzzy Imperialist Competitive Algorithm (FICA+FIS) are proposed to stock price prediction. To achieve this aim, two Fuzzy Inference Systems are designed to tuing the ICA’s parameters based on three effective factors in search strategy and to predict stock price based on 10 effective economic factors. The candidate fuzzy rules set of the inference engine is obtained by the FICA for the second FIS and six fuzzy rules of the first FIS are designed based on the ICA’s behaviour. The FICA+FIS has 10 inputs of the stock price variables including the lowest stock price, the highest stock price, the initial stock price, the trading volume, the trading value, the first market index of the trading floor, the total market price index, the dollar exchange rate, the global price per ounce of gold, the global oil price, and its output is also the stock price. The inputs and output variables consist of three linguistic vairables such as Low, Medium, and High with triangular membership functions. Each country (search agent) of the FICA contains information on all the fuzzy rules of the inference engine attributed to the country and has r×12 elements, where r is the number of fuzzy rules. The FICA’s objective function is the mean square error (MSE) to evaluate the power of each country. A challenge of the ICA is the proper tuning paprameters such as the Revolution Probability (Prevolve), Assimilation Coefficient (Beta) and the Colonies Mean Cost Coefficient (zeta), which has a great impact on the efficiency of the algorithm (precision and time of access to solution). These parameters are usually constant and according to different problems, they have different values and are given experimentally. In this paper, the parameters are tuned based on the number of iterations that the best objective function value has not improved (UN), the number of imperialist (Ni) and the current number iteration (Iter). To this aim, a FIS is designed based on six fuzzy rules that UN, Ni and Iter are its input variables and Prevolve, Beta and zeta are its output variables. To analyze the efficiency of the FICA+FIS as a case study, six datasets are collocted from six companies which were active between 1389 to 1394 in Tehran Stock Exchange such as Pars Oil, Iran Khodro, Motogen, Ghadir, Tidewater and Mobarakeh. The information of around 2000 days are collected for each company and the data are divided to train and test data based on cross validation 10-fold. To compare the performance of the FICA+FIS, two groups of stock price prediction methods were implemented. In the first group, the fuzzy rules of the FIS’s engine to stock price prediction are obtained by the classic draft of the Imperialist Competitive Algorithm (ICA+FIS), the Genetic Algorithm (GA+FIS) and the Whale Optimization Algorithm (WOA+FIS), which are used to compare with the FICA. The second group includes classic stock price prediction methods such as multi-layered neural network (NN), support vector machine (SVM), CART decision tree (DT-CART), random forest (RF) and Gaussian process regression (GPR), which are used to compare with the FICA+FIS. The experimental results show that first, the improved fuzzy draft of the ICA performed better than its classic draft, the GA and the WOA, and second, the performance of the FICA FIS is better than other investigated algorithms in both training and testing phases, although the DT is a competitor in the training phase and the RF is a competitor in the test phase on some datasets.

Solar radiation is one of the most salient factors in determining the optimal locations of solar farms. It is the main input of geological, ecological, meteorological and hydrological models. In Iran, there are 63 stations which measures solar radiation; compared to the extent of the country, solar radiation monitoring network has very low densities. In the present study, in order to increase the network congestion and continuous mapping of solar radiation, synoptic meteorological stations’ data were used. Considering the high correlation between solar radiation and meteorological data (sunshine duration, maximum temperature and negatively high correlated sea pressure), such data was used to calculate solar radiation in synoptic stations by using Fuzzy Inference System (FIS), Artificial Neural Network (ANN) and Adaptive Neuro Fuzzy Inference Systems (ANFIS). The evaluation of the results was performed by RMSE, MAE and MBE to rank the methods. Our results revealed that Sugeno method accompanied by Fuzzy C-mean clustering has RMSE=28.07 w/m2 that lays the least errors amongst the others. With respect to ANN, Cub-clustering and Grid partition ANFIS, Sugeno method showed 18, 39% and 42% improvement. MAE and MBE also implied the ability of the Sugeno fuzzy method. Such a method is more flexible for modeling complex and nonlinear systems. The implementation of the methods in prediction of solar radiation revealed that Sugeno is easier and faster to executable. Estimated Solar radiation for 333 synoptic stations was interpolated by Ordinary Kriging to generate a continuous surface for the country. The generated solar radiation atlas is suitable to identify solar throw areas of our country as well as for engineering applications and energy planning. Radiation atlas showed that 32 percent of the country has solar radiation above 500w/m2 that is the amount of radiation required for solar farms.

Presenting location-based services (LBSs) in mobile information environments has been developing quantitatively and qualitatively each day. The most important ingredient of such services is map. The fundamental map elements like information contents, scale and other graphical items are related to the spatial resolution of user or device. Thus, map resolution is an integral part of LBSs. One of the less-attended issues so far is determining the resolution of maps that are going to indicate some information to users. In this paper, a context-aware approach is presented to determine the resolution of maps used in LBSs. In fact, the map’s resolution is computed using fuzzy inference engine based on two basic contexts of location and time. In this method, user’s location and the time of request are inputs to the fuzzy inference system. The map’s resolution for indicating information to user is the system’s output. Fuzzy rule base is developed by grouping and consulting users. The proposed approach was implemented for a group of faculty members and the results proved the applicability of the method for other user groups.

This study examines a multi-objective fuzzy simplex-genetic algorithm which was developed to predict bank legal customers financial performance. Prediction performance of the model was examined based on its ability to accurately identify credit default. Using available data from KESHVARZI bank over 2001-2006, debt ratio, operational ratio, and return on equity are selected as descriptive variables, and on the other side dependent variable was considered as a dummy variable. To training and validating the model, data were divided in to model (in-sample) and test (out-of-sample) sets. After running the algorithm, besides the sensitivity and specificity ratios, the key variable was specified.