Integration of genetic fuzzy systems and artificial neural networks for stock price forecasting

Stock market prediction is regarded as a challenging task in financial time-series forecasting. The central idea to successful stock market prediction is achieving best results using minimum required input data and the least complex stock market model. To achieve these purposes this article presents an integrated approach based on genetic fuzzy systems (GFS) and artificial neural networks (ANN) for constructing a stock price forecasting expert system. At first, we use stepwise regression analysis (SRA) to determine factors which have most influence on stock prices. At the next stage we divide our raw data into k clusters by means of self-organizing map (SOM) neural networks. Finally, all clusters will be fed into independent GFS models with the ability of rule base extraction and data base tuning. We evaluate capability of the proposed approach by applying it on stock price data gathered from IT and Airlines sectors, and compare the outcomes with previous stock price forecasting methods using mean absolute percentage error (MAPE). Results show that the proposed approach outperforms all previous methods, so it can be considered as a suitable tool for stock price forecasting problems.     

Estimation of magnitude and time duration of temporary overvoltages using ANN in transmission lines during power system restoration

In most countries, the main step in the process of power system restoration, following a complete/partial blackout, is energization of primary restorative transmission lines. Artificial neural network (ANN) is employed for performing a nonlinear input–output mapping in this work, in order to estimate the temporary overvoltages (TOVs) due to transmission lines energization. In the proposed methodology, Levenberg–Marquardt second order method is used to train the multilayer perceptron. Proposed ANN is trained with equivalent circuit parameters of the network as input parameters, trained ANN has therefore satisfactory generalization capability. Both single and three-phase line energizations are analyzed. The simulated results for 39-bus New England test system, indicate that the proposed technique can estimate the peak values and duration of switching overvoltages with acceptable accuracy.     

Deriving operating policies for multi-objective reservoir systems: Application of Self-Learning Genetic Algorithm

Optimal multi-reservoir operation is a multi-objective problem in nature and some of its objectives are nonlinear, non-convex and multi-modal functions. There are a few areas of application of mathematical optimization models with a richer or more diverse history than in reservoir systems optimization. However, actual implementations remain limited or have not been sustained.Genetic Algorithms (GAs) are probabilistic search algorithms that are capable of solving a variety of complex multi-objective optimization problems, which may include non-linear, non-convex and multi-modal functions. GA is a population based global search method that can escape from local optima traps and find the global optima. However GAs have some drawbacks such as inaccuracy of the intensification process near the optimal set.In this paper, a new model called Self-Learning Genetic Algorithm (SLGA) is presented, which is an improved version of the SOM-Based Multi-Objective GA (SBMOGA) presented by Hakimi-Asiabar et al. (2009) [45]. The proposed model is used to derive optimal operating policies for a three-objective multi-reservoir system. SLGA is a new hybrid algorithm which uses Self-Organizing Map (SOM) and Variable Neighborhood Search (VNS) algorithms to add a memory to the GA and improve its local search accuracy. SOM is a neural network which is capable of learning and can improve the efficiency of data processing algorithms. The VNS algorithm can enhance the local search efficiency in the Evolutionary Algorithms (EAs).To evaluate the applicability and efficiency of the proposed methodology, it is used for developing optimal operating policies for the Karoon-Dez multi-reservoir system, which includes one-fifth of Iran's surface water resources. The objective functions of the problem are supplying water demands, generating hydropower energy and controlling water quality in downstream river.     

Adaptive robust control of uncertain systems with state and input delay

In this paper, adaptive robust control of uncertain systems with multiple time delays in states and input is considered. It is assumed that the parameter uncertainties are time varying norm-bounded whose bounds are unknown but their functional properties are known. To overcome the effect of input delay on the closed loop system stability, new Lyapunov Krasovskii functional will be introduced. It is shown that the proposed adaptive robust controller guarantees globally uniformly exponentially convergence of all system solutions to a ball with any certain convergence rate. Moreover, if there is no disturbance in the system, asymptotic stability of the closed loop system will be established. The proposed design condition is formulated in terms of linear matrix inequality (LMI) which can be easily solved by LMI Toolbox in Matlab. Finally, an illustrative example is included to show the effectiveness of results developed in this paper.     

A hybrid method for flowshops scheduling with condition-based maintenance constraint and machines breakdown

One of the most important assumptions in production scheduling is that the machines are permanently available without any breakdown. In the real world of scheduling, machines can be made unavailable due to various reasons such as preventive maintenance and unpredicted breakdown. In this paper, we explore flowshop configuration under the assumption of condition-based maintenance to minimize expected makespan. Furthermore, we consider a condition-based maintenance (CBM) strategy which could be used in most industrial settings. The proposed algorithm is designed for non-resumable flowshop state where the processing of jobs after preventive maintenance is restarted from the beginning. We propose a hybrid algorithm based on genetic algorithm and simulated annealing. Additionally, we conduct an extensive parameter calibration with the utilization of Taguchi method and select the optimal levels of the algorithm’s performance influential factors. The preliminary results indicate that the proposed method provides significantly better results compared with other high performing algorithms in the literature.     

Is information systems a discipline? Foucauldian and Toulminian insights

Following the Kleinian spirit, this study takes a critical view of the existing orthodoxy within information systems (IS) and reframes the ongoing discussion concerning the intellectual core, identity and disciplinary status of IS using the disciplinary analysis of Michel Foucault and Stephen Toulmin. Instead of limiting the discussion to specific paradigms, topics, subjects or content, it focuses on the characteristics, rules and goals of IS as an academic field. A disciplinary lens is used to frame what it means to be a field, discipline and science, and in the process the study uncovers four doxas that have shaped the development of the IS field: (1) the IS research community sees no difference between fields, disciplines or sciences; (2) IT changes so rapidly, and thus the IS field needs to change to remain relevant; (3) disciplines are by definition rigid, inflexible and uni-theoretical and (4) because IS is pluralistic, IS should not become a discipline. This study's analyses of the IS field's discursive formation and intellectual ideals offer novel perspectives that allow for the integration of the IS field's plurality and diversity. To transform the IS field from its multimodal existence into a vibrant, diverse, academically and socially relevant and influential discipline, the study proposes actionable strategies that include (1) agreeing on the intellectual ideals for IS, (2) focusing on conceptual formation, (3) focusing on theory construction, (4) erecting genealogical boundaries and (5) fostering the development of professional bodies.     

A study of the impact of the willingness-to-learn of CAD novice users on their competence development

The notion that ‘attitude drives behavior’ manifests itself in a variety of ways in educational and occupational settings. As applied to CAD competence development, industrial training of novice CAD users on their way to becoming competent CAD users consume a lot of corporate resources. This paper is the third paper in the line of research that attempts to answer the question having to do with what it takes to make a competent CAD user. Specifically, we examine the CAD-specific factors revolving around the trainees’ willingness-to-learn CAD.These factors are analyzed in two stages. At the start of the training, trainees’ initial attitude towards CAD is established by means of a short questionnaire. Afterwards, throughout the training, trainees’ behavior (online and offline practice) is gauged and, in turn, a relation is established to illustrate how this practice leads to the development of CAD-specific skills. For this purpose, another short questionnaire was utilized. Strong correlations were established relating the trainees’ CAD-specific behavior with the CAD-specific outcomes of learning CAD syntax.Furthermore, and in order to assess the quality of the trainees’ learning of CAD, overall competence was monitored throughout the study via performance measures that describe the time it took the trainees to build test models (speed), which reflects upon the ability to learn the syntax of the CAD tool (declarative knowledge). The sophistication of the models is also used as another measure. Correlating the trainees’ character attributes with these assessed measures, it was found that the stronger is the trainees’ will to learn CAD, the stronger is the likelihood to learn faster. Perhaps more importantly, trainees with initial favorable attitude toward CAD were shown to develop increasingly positive behavior that manifested through additional practice and other forms of visible effort.     

Application of neural-fuzzy controller for streaming video over Bluetooth 1.2

This paper is to introduce an application of Computational Intelligence (CI) to Moving Picture Expert Group-4 (MPEG-4) video compression over IEEE.802.15.1 wireless communication, known as Bluetooth 1.2, in order to improve picture quality. The 2.4 GHz Industrial, Scientific and Medical frequency band is used for the IEEE.802.15.1 standard. IEEE.802.15.1 can be affected by noise and interference due to other neighboring wireless devices sharing the same frequency carrier. The noise and interference create difficulties in ascertaining an accurate real-time transmission rate at the receiving end. Furthermore, the MPEG-4 codec is an object-oriented compression system and demands a high bandwidth. It is therefore difficult to avoid excessive delay, image quality degradation and/or data loss during MPEG-4 video transmission over standard systems. A new buffer entitled ‘buffer added’ has been introduced at the input of the Bluetooth 1.2 device. This buffer is controlled by a Rule-Based Fuzzy (RBF) logic controller at the input and a neural-fuzzy controller (NFC) at the output. The two new fuzzy rules manipulate and supervise the flow of video over the Bluetooth 1.2 standard. The computer simulation results illustrate the comparison between a non-CI video transmission over Bluetooth 1.2 and the proposed design, confirming that the applications of RBF and NFC do improve the image quality, reduce data loss and reduce time delay.     

Design, fabrication and control of a magnetic capsule-robot for the human esophagus

Much research on the development of a robotic capsule and micro robot for the diagnosis of gastrointestinal diseases has been carried out. The powering of these micro systems is becoming very challenging as the implementation of such systems is limited due to the existence of on-board power supplies. This paper presents a micro robotic system based on magnetic principles. The goal is to build a system in which a capsule-robot can be manipulated wirelessly inside an enclosed environment such as human??s body. A prototype of capsule-robot is built and tested, that can be remotely operated with three DOF in an enclosed environment by transferring magnetic energy and electromagnetic waves. A magnetic drive unit generates magnetic energy for the manipulation. Experimental results show the capsule-robot is manipulated and moved through a desired trajectory in a viscous fluid. The capsule-robot can be potentially used for endoscopy and colonoscopy.     

A parameter-tuned genetic algorithm for the resource investment problem with discounted cash flows and generalized precedence relations

A resource investment problem with discounted cash flows (RIPDCF) is a project-scheduling problem in which (a) the availability levels of the resources are considered decision variables and (b) the goal is to find a schedule such that the net present value of the project cash flows optimizes. In this paper, the RIPDCF in which the activities are subject to generalized precedence relations is first modeled. Then, a genetic algorithm (GA) is proposed to solve this model. In addition, design of experiments and response surface methodology are employed to both tune the GA parameters and to evaluate the performance of the proposed method in 240 test problems. The results of the performance analysis show that the efficiency of the proposed GA method is relatively well.