Direct adaptive fuzzy control with membership function tuning

In this paper, a direct self‐structured adaptive fuzzy control is introduced for the class of nonlinear systems with unknown dynamic models. Control is accomplished by an adaptive fuzzy system with a fixed number of rules and adaptive membership functions. The reference signal and state errors are used to tune the membership functions and update them instantaneously. The Lyapunov synthesis method is also used to guarantee the stability of the closed loop system. The proposed control scheme is applied to an inverted pendulum and a magnetic levitation system, and its effectiveness is shown via simulation. Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Laboratory Investigation of Stilling Basin Slope Effect on Bed Scour at Downstream of Stepped Spillway: Physical Modeling of Javeh RCC Dam

Stepped spillway and stilling basin are one of the most important energy dissipation structures. Eventhough, most of energy dissipated by these structures, but in skimming flow, the upstream flow motion is nonaerated and the residual energy capable to destroyed structures during floods. In this study, effect of stilling basin slope on bed scour, downstream of Javeh dam was investigating. Experiments performed in hydraulic structures laboratory of the University of Kerman with six different discharges (5, 7, 13, 17, 25 and 30 l/s.m) and five various stilling basin slope (0.02, 0.01, 0, ?0.01 and???0.02). The parameters such as maximum scour depth (d_s), flow velocity (in three point), water depth on upstream and downstream of stepped spillway and stilling basin, the distance of the maximum scour depth to sill (L_s) and the gheometery of scour hole measured. Result shown that when stilling basin slopes was 0.02, the average of maximum relative scour depth, 47% Increased and in ?0.02, 52.2% Decreased. In addition, the distance of maximum scour depth until stilling basin increased by increasing and decreased by decreasing the stilling basin slope.

     

An adaptive neuro‐fuzzy system for stock portfolio analysis

We propose an adaptive neuro‐fuzzy inference system (ANFIS) for stock portfolio return prediction. Previous work has shown that portfolio optimization can be improved by using predicted stock earnings rather than historical earnings. We show that predicted portfolio returns can be improved by using ANFIS and taking as input a variety of technical and fundamental attributes about various indices of the stock market. To generate membership functions, we use a robust noise rejection‐clustering algorithm. The neuro‐fuzzy model is tested on portfolios constituted from the Tehran Stock Exchange. In our experiments, the proposed method performs better in predicting the portfolio return than the classical Markowitz portfolio optimization method, a multiple regression, a neural network, and the Sugeno–Yasukawa method. © 2010 Wiley Periodicals, Inc.     

A fast and noise resilient cluster-based anomaly detection

Clustering, while systematically applied in anomaly detection, has a direct impact on the accuracy of the detection methods. Existing cluster-based anomaly detection methods are mainly based on spherical shape clustering. In this paper, we focus on arbitrary shape clustering methods to increase the accuracy of the anomaly detection. However, since the main drawback of arbitrary shape clustering is its high memory complexity, we propose to summarize clusters first. For this, we design an algorithm, called Summarization based on Gaussian Mixture Model (SGMM), to summarize clusters and represent them as Gaussian Mixture Models (GMMs). After GMMs are constructed, incoming new samples are presented to the GMMs, and their membership values are calculated, based on which the new samples are labeled as “normal” or “anomaly.” Additionally, to address the issue of noise in the data, instead of labeling samples individually, they are clustered first, and then each cluster is labeled collectively. For this, we present a new approach, called Collective Probabilistic Anomaly Detection (CPAD), in which, the distance of the incoming new samples and the existing SGMMs is calculated, and then the new cluster is labeled the same as of the closest cluster. To measure the distance of two GMM-based clusters, we propose a modified version of the Kullback–Libner measure. We run several experiments to evaluate the performances of the proposed SGMM and CPAD methods and compare them against some of the well-known algorithms including ABACUS, local outlier factor (LOF), and one-class support vector machine (SVM). The performance of SGMM is compared with ABACUS using Dunn and DB metrics, and the results indicate that the SGMM performs superior in terms of summarizing clusters. Moreover, the proposed CPAD method is compared with the LOF and one-class SVM considering the performance criteria of (a) false alarm rate, (b) detection rate, and (c) memory efficiency. The experimental results show that the CPAD method is noise resilient, memory efficient, and its accuracy is higher than the other methods.     

Solving non-convex economic dispatch problem with valve point effects using modified group search optimizer method

This paper presents a novel solution based on the group search optimizer (GSO) methodology in order to determine the feasible optimal solution of the economic dispatch (ED) problem considering valve loading effects. The basic disadvantage of the original GSO algorithm is the fact that it gives a near-optimal solution rather than an optimal one in a limited runtime period. In this paper, a new modified group search optimizer (MGSO) is presented for improving the scrounger and ranger operators of GSO. The proposed MGSO is applied on different test systems and compared with most of the recent methodologies. The results show the effectiveness of the proposed method and prove that MGSO can be applicable for solving the power system economic load dispatch problem, especially in large scale power systems.     

Tartaric Acid as a Non-toxic and Environmentally-Friendly Anti-scaling Material for Using in Cooling Water Systems: Electrochemical and Surface Studies

Because of the major limitations in drinking water resources, the industries need to use unprocessed water sources for their cooling systems; these water resources contain major amount of hardening cations. So, mineral scales are formed in cooling water systems during the time and cause major problems. The use of green anti-scaling materials such as carboxylic acids is considered due to their low risks of environmental pollution. In the present work, the scale inhibition performance of tartaric acid as a green organic material was evaluated. Chemical screening tests, cathodic and anodic voltammetry measurements and electrochemical impedance spectroscopy (EIS), field emission scanning electron microscopy (FESEM), energy-dispersive x-ray and x-ray diffraction, were used for the evaluation of the scale inhibition performance. The results showed that tartaric acid can prevent calcium carbonate precipitation significantly. The hard water solution with 2.0 mM of tartaric acid indicated the highest scale inhibition efficiency (ca. 68%). The voltammetry, EIS and FESEM results verified that tartaric acid can form smooth and homogeneous film on steel surface through formation of Fe(III)-tartrate complexes and retard the local precipitation of calcium carbonate deposits.     

An investigation on the sensitivity analysis of the parameters of proposed wax precipitation model

Reservoir hydrocarbon fluids contain heavy paraffins that may form solid phase of wax at low temperatures. Formation of solid phases is highly unwanted in oil production assemblies, pipelines and in process equipments. A predictive technique is crucial to the solution of wax formation to alleviate this problem.The effect of different parameters to predict the conditions under which wax precipitation takes place using the proposed model of Sahand University of Technology and other models has been investigated. The proposed model uses regular solution theory to describe solid phase (wax) non-ideality and the liquid and gas phases are being described by an equation of state.In order to evaluate the reliability of the proposed model, wax appearance temperatures (WAT's) were calculated for several mixtures at different compositions and compared with different models. The proposed model predictions had very good agreement with experimental data over a wide range of compositional distributions in comparison with other models. Solid wax content was also calculated at different temperatures below WAT in several synthetic systems made up of a solvent (decane) and a paraffinic heavy fraction. The results of calculating the amount of wax precipitation showed very good agreement with experimental data. Effect of different parameters including fusion temperature (T^f), Enthalpy of fusion (Δh^f), solubility parameter (δ^S), and binary interaction parameters (BIP) in predicting the WAT and the amount of wax precipitated for different oil mixtures have been evaluated using the proposed model and compared with other models. The results showed that the T^f is the most sensitive parameter while δ^S shows the least sensitivity in matching the WAT. Even though using Δh^f could provide the same results as tuning T^f, but the required changes are much higher and sometimes not practical. Also using BIP as the tuning parameter, requires a fairly large coefficient that makes it unsuitable to be considered as the tuning parameter.     

A semantic social network-based expert recommender system

This research work presents a framework to build a hybrid expert recommendation system that integrates the characteristics of content-based recommendation algorithms into a social network-based collaborative filtering system. The proposed method aims at improving the accuracy of recommendation prediction by considering the social aspect of experts’ behaviors. For this purpose, content-based profiles of experts are first constructed by crawling online resources. A semantic kernel is built by using the background knowledge derived from Wikipedia repository. The semantic kernel is employed to enrich the experts’ profiles. Experts’ social communities are detected by applying the social network analysis and using factors such as experience, background, knowledge level, and personal preferences. By this way, hidden social relationships can be discovered among individuals. Identifying communities is used for determining a particular member’s value according to the general pattern behavior of the community that the individual belongs to. Representative members of a community are then identified using the eigenvector centrality measure. Finally, a recommendation is made to relate an information item, for which a user is seeking an expert, to the representatives of the most relevant community. Such a semantic social network-based expert recommendation system can provide benefits to both experts and users if one looks at the recommendation from two perspectives. From the user’s perspective, she/he is provided with a group of experts who can help the user with her/his information needs. From the expert’s perspective she/he has been assigned to work on relevant information items that fall under her/his expertise and interests.     

Hybrid binary arithmetic optimization algorithm with simulated annealing for feature selection in high-dimensional biomedical data

Gene expression data play a significant role in the development of effective cancer diagnosis and prognosis techniques. However, many redundant, noisy, and irrelevant genes (features) are present in the data, which negatively affect the predictive accuracy of diagnosis and increase the computational burden. To overcome these challenges, a new hybrid filter/wrapper gene selection method, called mRMR-BAOAC-SA, is put forward in this article. The suggested method uses Minimum Redundancy Maximum Relevance (mRMR) as a first-stage filter to pick top-ranked genes. Then, Simulated Annealing (SA) and a crossover operator are introduced into Binary Arithmetic Optimization Algorithm (BAOA) to propose a novel hybrid wrapper feature selection method that aims to discover the smallest set of informative genes for classification purposes. BAOAC-SA is an enhanced version of the BAOA in which SA and crossover are used to help the algorithm in escaping local optima and enhancing its global search capabilities. The proposed method was evaluated on 10 well-known microarray datasets, and its results were compared to other current state-of-the-art gene selection methods. The experimental results show that the proposed approach has a better performance compared to the existing methods in terms of classification accuracy and the minimum number of selected genes.