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.     

MFLP: a low power encoding for on chip networks

Network on chip (NoC) has been proposed as an appropriate solution for today’s on-chip communication challenges. Power dissipation has become a key factor in the NoCs because of their shrinking sizes. In this paper, we propose a new encoding approach aimed at power reduction by decreasing the number of switching activities on the buses. This approach assigns the symbols to data word in such a way that the more frequent words are sent by less power consumption. This algorithm dedicates the symbols with less ones to high probability data and uses transition signaling to transmit data. The proposed method, unlike the existing low power encoding, does not rely on spatial redundancy and keeps the width of the bus constant. Experimental evaluations show that our approach reduces the power dissipation up to 46 % with 2.70, 0.51, and 15.43 % power, critical path and area overhead in the NoCs, respectively.     

Prediction of carbon dioxide separation from gas mixtures in petroleum industries using the Levenberg–Marquardt algorithm

In this study, two mathematical models for hydrate formation process to separate carbon dioxide by a combination of two different kinds of organic and surfactant promoters are presented. Promoters such as sodium dodecyl sulfate, sodium dodecyl benzene sulfonate, and dodecyl trimethyl ammonium chloride as surfactant promoters; also, tetrahydrofuran, cyclopentane, 1,3-dioxolane, and 2-methyl tetrahydrofuran as organic promoters have been used in recent years. The results showed that a combination of 3000 ppm of surfactant promoters and 4 wt% organic promoters had the highest separation rate of carbon dioxide and; consequently, the investigated models were based on this optimum condition. As a matter of fact, by using these simulations the hydrate formation behavior was predicted with high accuracy; moreover, conducting consuming experiments is not essential anymore. To sum up, in the present research both Vandermonde matrix model and Levenberg-Marquardt algorithm were applied to predict the hydrate formation behavior; in addition, their results were precisely considered and validated with experimental data.     

Robust Diversity-based Sine-Cosine Algorithm for Optimizing Hydropower Multi-reservoir Systems

Hydropower energy generation depends on the available water resources. Therefore, planning and operation of the water resource systems are paramount tasks for energy management. Since reservoirs are one of the important components of water resources systems, extracting optimal operating policies for proper management of energy generated from these systems is an imperative step. Optimizing reservoir system operation (ORSO) is a non-linear, large-scale, and non-convex problem with a large number of constraints and decision variables. To solve ORSO problem effectively, a robust diversity-based, sine-cosine algorithm (RDB-SCA) is developed in the present study by introducing several strategies to balance the global exploration and local exploitation ability and to achieve accurate and reliable solutions. An efficient linear operation rule is coupled with the RDB-SCA to maximize the energy generation. The proposed method is then applied to a real-world, multi-reservoir system to extract optimal operational policies and, consequently, maximize the energy production. It is shown that the RDB-SCA is able to generate 24, 14, and 6% more energy than the original SCA, respectively for 2-, 3-, and 4-reservoir systems. The present findings are useful to suggest guidelines for efficient operation of hydropower multi-reservoir systems. This paper is supported by https://imanahmadianfar.com/codes.

     

Reductive removal of hexavalent chromium from aqueous solution using sepiolite-stabilized zero-valent iron nanoparticles: Process optimization and kinetic studies

We studied the optimization of hexavalent chromium (Cr(VI)) removal from aqueous solution using the synthesized zero-valent iron nanoparticles stabilized with sepiolite clay (S-ZVIN), under various parameters such as reaction time (min), initial solution pH and concentration of S-ZVIN (g·L^?1) using response surface methodology (RSM). The kinetic study of Cr(VI) was conducted using three types of the most commonly used kinetic models including pseudo zero-order, pseudo first-order, and pseudo second-order models. The rate of reduction reaction showed the best fit with the pseudo first-order kinetic model. The process optimization results revealed a high agreement between the experimental and the predicted data (R²=0.945, Adj-R²=0.890). The results of statistical analyses showed that reaction time was the most impressive factor influencing the efficiency of removal process. The optimum conditions for maximum response (98.15%) were achieved at the initial pH of 4.7, S-ZVIN concentration of 1.3 g·L^?1 and the reaction time of 75 min.     

Direct power control of DFIG based on discrete space vector modulation

This paper presents a new direct power control (DPC) strategy for a double fed induction generator (DFIG) based wind energy generation system. Switching vectors for rotor side converter were selected from the optimal switching table using the estimated stator flux position and the errors of the active and reactive power. A few number of voltage vectors may cause undesired power and stator current ripple. In this paper the increased number of voltage vectors with application of the Discrete Space Vector Modulation (DSVM) will be presented. Then a new switching table in supersynchronous and subsynchronous frames will be proposed. Simulation results of a 2 MW DFIG system demonstrate the effectiveness and robustness of the proposed control strategy during variations of active and reactive power, machine parameters, and wind speed.     

No-wait two stage hybrid flow shop scheduling with genetic and adaptive imperialist competitive algorithms

This article studies a no-wait two-stage flexible flow shop scheduling problem with setup times aiming to minimize the total completion time. The problem is solved using an adaptive imperialist competitive algorithm (AICA) and genetic algorithm (GA). To test the performance of the proposed AICA and GA, the algorithms are compared with ant colony optimisation, known as an effective algorithm in the literature. The performance of the algorithms are evaluated by solving both small and large-scale problems. Their performance is evaluated in terms of relative percentage deviation. Finally the results of the study are discussed and conclusions and potential areas for further study are highlighted.     

Scaling of analog LDPC decoders in sub-100 nm CMOS processes

Analog implementations of digital error control decoders, generally referred to as analog decoding, have recently been proposed as an energy and area competitive methodology. Despite several successful implementations of small analog error control decoders, little is currently known about how this methodology scales to smaller process technologies and copes with the non-idealities of nano-scale transistor sizing. A comprehensive analysis of the potential of sub-threshold analog decoding is examined in this paper. It is shown that mismatch effects dominated by threshold mismatch impose firm lower limits on the sizes of transistors. The effect of various forms of leakage currents is also investigated and minimal leakage current to normalizing currents are found using density evolution and control simulations. Finally, the convergence speed of analog decoders is examined via a density evolution approach. The results are compiled and predictions are given which show that process scaling below 90 nm processes brings no advantages, and, in some cases, may even degrade performance or increase required resources.