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Extracting Classification Rules from Artificial Neural Network Trained with Discretized Inputs

NEURAL PROCESSING LETTERS 2020年第3期52卷 2469-2491页

作者： Yedjour, Dounia USTO MB Fac Math & Comp Sci Dept Comp Sci BP 1505 El Mnaouer 31000 Oran Algeria

Rule extraction from artificial neural networks remains important task in complex diseases such as diabetes and breast cancer where the rules should be accurate and comprehensible. The quality of rules is improved by the improvement of the network classification accuracy which is done by the discretization of input attributes. In this paper, we developed a rule extraction algorithm based on multiobjective genetic algorithms and association rules mining to extract highly accurate and comprehensible classification rules from ANN's that have been trained using the discretization of the continuous attributes. The data pre-processing provides very good improvement of the ANN accuracy and consequently leads to improve the performance of the classification rules in terms of fidelity and coverage. The results show that our algorithm is very suitable for medical decision making, so an excellent average accuracy of 94.73 has been achieved for the Pima dataset and 99.36 for the breast cancer dataset.

关键词： Neural network Rule extraction multiobjective genetic algorithm Discretized attributes

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Improving distance based image retrieval using non-dominated sorting genetic algorithm

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PATTERN RECOGNITION LETTERS 2015年第Feb.1期53卷 109-117页

作者： Arevalillo-Herraez, Miguel Ferri, Francesc J. Moreno-Picot, Salvador Univ Valencia Dept Informat E-46100 Burjassot Spain

Relevance feedback has been adopted as a standard in Content Based Image Retrieval (CBIR). One major difficulty that algorithms have to face is to achieve and adequate balance between the exploitation of already known areas of interest and the exploration of the feature space to find other relevant areas. In this paper, we evaluate different ways to combine two existing relevance feedback methods that place unequal emphasis on exploration and exploitation, in the context of distance based methods. The hybrid approach proposed has been evaluated by using three image databases of various sizes that use different descriptors. Results show that the hybrid technique performs better than any of the original methods, highlighting the benefits of combining exploitation and exploration in relevance feedback tasks. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Image retrieval multiobjective genetic algorithm Relevance feedback

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Development of a multiobjective GA for advanced planning and scheduling problem

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2009年第9-10期42卷 974-992页

作者： Dayou, Liu Pu, Yan Ji, Yu Jilin Univ Coll Comp Sci & Technol Minist Educ Key Lab Symbol Computat & Knowledge Engn Changchun 130012 Peoples R China

In this paper, we consider an advanced planning and scheduling (APS) problem in manufacturing supply chain. The problem was formulated with mixed integer programming and three objectives are taken into account. To solve the APS model, a multiobjective genetic algorithm with local search is presented to find the Pareto optimal solutions. The proposed algorithm makes use of the principle of nondominated sorting, coupled with the use of a metric for normalized crowding distance. Local search technique is used to improve the efficiency. The proposed algorithm was compared with two other multiobjective genetic algorithms from the literature. Performance of these heuristics has been tested on ten problems in three scenarios. The computational results demonstrate the effectiveness and efficiency of the proposed approach and indicate that the presented algorithm outperforms previous work for APS problems.

关键词： Advanced planning multiobjective genetic algorithm genetic local search

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Multi objective outbound logistics network design for a manufacturing supply chain

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JOURNAL OF INTELLIGENT MANUFACTURING 2013年第6期24卷 1071-1084页

作者： Hiremath, N. C. Sahu, Sadananda Tiwari, Manoj Kumar Indian Inst Technol Kharagpur Dept Ind Engn & Management Kharagpur 721302 W Bengal India

Outbound logistics network (OLN) in the downstream supply chain of a firm plays a dominant role in the success or failure of that firm. This paper proposes the design of a hybrid and flexible OLN in multi objective context. The proposed distribution network for a manufacturing supply chain consists of a set of customer zones (CZs) at known locations with known demands being served by a set of potential manufacturing plants, a set of potential central distribution centers (CDCs), and a set of potential regional distribution centers (RDCs). Three variants of a single product classified based on nature of demand are supplied to CZs through three different distribution channels. The decision variables include number of plants, CDCs, RDCs, and quantities of each variant of product delivered to CZs through a designated distribution channel. The goal is to design the network with multiple objectives so as to minimize the total cost, maximize the unit fill rates, and maximize the resource utilization of the facilities in the network. The problem is formulated as a mixed integer linear programming problem and a multiobjective genetic algorithm (MOGA) called non-dominated sorting genetic algorithm-II (NSGA-II) is employed to solve the resulting NP-hard combinatorial optimization problem. Computational experiments conducted on randomly generated data sets are presented and analyzed showing the effectiveness of the solution algorithm for the proposed network.

关键词： Outbound logistics network (OLN) Outbound logistics network design (OLND) Manufacturing supply chain Mixed integer linear programming multiobjective genetic algorithm NSGA-II

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A nonparametric approach using artificial intelligence in vibration and noise reduction of flexible systems

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2014年第8期228卷 1329-1347页

作者： Zolfagharian, Ali Noshadi, Amin Ghasemi, Seyed Ebrahim Zain, Mohd Zarhamdy Md Univ Teknol Malaysia Dept Syst Dynam & Control Johor Baharu 81310 Malaysia Victoria Univ Sch Sci & Engn Fac Hlth Engn & Sci Melbourne Vic 8001 Australia Babol Univ Technol Dept Mech Engn Babol Sar Iran

The main aim of this paper is to broaden the application's area of artificial intelligence including fuzzy logic and multiobjective evolutionary algorithm into real-time control area. Wiper system is a high order, nonlinear model with single-input and multi-outputs so that rise time, maximum overshoot, and end-point vibration of wiper blade are observed in conflict as the faster response leads to the larger level of undesired noise and vibration. The first part of this paper centers acquiring experimental data from a passenger automobile wiper system during its operation and using a reliable nonlinear system identification, namely, nonlinear autoregressive exogenous Elman neural network. Knowing that in a practical environment, where the loading conditions of the flexible wiper blade may be varied due to rain, snow, or wind lift in high-speed driving, causing changes in the characteristics of the system, the system performance with a fixed conventional controller scheme will not be satisfactory. The main contribution of this work is presented in second part where a novel multiobjective, bilevel adaptive-fuzzy controller is proposed for an automobile wiper system. The system's parameters are tuned simultaneously by a multiobjective genetic algorithm based on fitness sharing whereby an automobile wiper blade is moved within its sweep workspace in the least amount of time with minimum noise and vibration.

关键词： Automotive wiper nonlinear system identification fuzzy logic iterative learning multiobjective genetic algorithm

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ANN-Based STATCOM Tuning for Performance Enhancement of Combined Wind Farms

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2019年第1-2期47卷 10-26页

作者： Rashad, Ahmed Kamel, Salah Jurado, Francisco Abdel-Nasser, Mohamed Mahmoud, Karar Aswan Univ Fac Engn Dept Elect Engn Aswan Egypt Univ Jaen Dept Elect Engn Jaen Spain Chongqing Univ Coll Elect Engn State Key Lab Power Transmiss Equipment & Syst Se Chongqing Peoples R China

Although the wind farms based on squirrel cage induction generators (SCIG) is cheaper than the wind farms based on doubly fed induction generators (DFIG), it is always in desperate need for reactive power compensation. Nevertheless, the wind farms based on DFIG are expensive compared with the SCIG wind farm, it features by its ability to control the active power independent of reactive power. However, combined wind farm (CWF) has been developed to collect the benefits of SCIG and DFIG wind turbines in the same wind farm. In this article, artificial neural network (ANN) is used to evaluate gain parameters of static synchronous compensator (STATCOM) in order to improve the stability performance of CWF. The impact of tuned STATCOM on the performance of CWF during gust wind speed and during three-phase fault is comprehensively investigated. The performance of CWF with STATCOM tuned by ANN is compared with its performance when the STATCOM tuned by the multiobjective genetic algorithm (MOGA) and whale optimization algorithm (WOA). The results show that the performance of CWF can be enhanced using STATCOM tuned by ANN more than MOGA and WOA.

关键词： squirrel cage induction generators doubly fed induction generators combined wind farm STATCOM artificial neural network multiobjective genetic algorithm whale optimization algorithm

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A multiobjective Coverage-Based Model for Civilian Search and Rescue

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NAVAL RESEARCH LOGISTICS 2011年第3期58卷 167-179页

作者： Asiedu, Yaw Rempel, Mark Def Res & Dev Canada Ctr Operat Res & Anal Ottawa ON Canada

The Civil Air Search and Rescue Association (CASARA) is a Canada-wide volunteer aviation association that provides air search support services to the Canadian National Search and Rescue (SAR) program. As with any emergency service provider, the locations of CASARA units greatly impact their overall effectiveness. In this article, the optimal location of CASARA units is formulated as a multiobjective maximal covering location problem. The model addresses the objectives of maximizing the coverage, minimizing the number of units, and maximizing the backup coverage of SAR incidents within Canada. A multigender genetic algorithm is proposed to determine a set of nondominated CASARA location configurations. Results are compared with solutions found using commercial integer programming software. It is shown that the nondominated genetic algorithm solutions are near-optimal. These are determined in much less time than comparable solutions using commercial integer programming software. (C) 2009 Wiley Periodicals, Inc.*Naval Research Logistics 58: 167-179, 2011

关键词： search and rescue covering model multiobjective genetic algorithm

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Complex componential approach for redundancy allocation problem solved by simulation-optimization framework

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JOURNAL OF INTELLIGENT MANUFACTURING 2014年第4期25卷 661-680页

作者： Mariano, Carlos Henrique Fernando Kuri-Morales, Angel UTFPR Curitiba Parana Brazil Univ Nacl Autonoma Mexico Mexico City DF Mexico ITAM IIMAS UNAM San Angel Mexico City DF Mexico

This article addresses the problem of redundancy and reliability allocation in the operational dimensioning of an automated production system. The aim of this research is to improve the global reliability of the system by allocating alternative components (redundancies) that are associated in parallel with each original component. By considering a complex componential approach that simultaneously evaluates the interrelations among sub-systems, conflicting goals, and variables of different natures, a solution for the problem is proposed through a multi-objective formulation that joins a multi-objective elitist genetic algorithm with a high-level simulation environment also known as simulation optimization framework.

关键词： Reliability optimization Redundancy allocation problem Simulation optimization framework multiobjective optimization multiobjective genetic algorithm

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Leakage reduction in water distribution system using efficient pressure management techniques. Case study: Nagpur, India

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WATER SCIENCE AND TECHNOLOGY-WATER SUPPLY 2018年第6期18卷 2015-2027页

作者： Gupta, Aditya Dinesh Kulat, Kishore Visvesvaraya Natl Inst Technol Nagpur Maharashtra India

Leakages in water distribution system (WDS) are directly proportional to its operating pressure. Pressure management is becoming an important technique for reducing leakages in the water networks. This paper presents a pressure management technique for leakage reduction in north central WDS of Nagpur City, India, using variable speed pump and pressure reducing valves (PRVs). Variable speed pump is utilized for eliminating pressure deficiency during high demand and for reducing excess pressure causing leakage reduction during lower demand, by controlling the pump speed. PRVs have been used for further leakage reduction. This paper proposes a modified reference pressure algorithm for determining the location of valves in WDS. A multiobjective genetic algorithm (NSGA-II) is used to determine the optimized control value of pressure reducing valve with respect to change in demand pattern and to minimize the leakage rate in the WDS. Proposed pressure management technique leads to leakage reduction of 16.57% to 26.30% with respect to changes in demand pattern, causing daily average saving of 5.066 Ml. Minimum required pressure is maintained on every demand nodes to avoid pressure deficiency in WDS.

关键词： multiobjective genetic algorithm pressure management pressure reducing valves water distribution system (WDS)

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Design and Optimization of LNA Topologies with Image Rejection Filters

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INTERNATIONAL JOURNAL OF RF AND MICROWAVE COMPUTER-AIDED ENGINEERING 2010年第3期20卷 286-297页

作者： Golmakani, Abbas Mafinejad, Khalil Kouzani, Abbas Sadjad Inst Higher Educ Dept Elect Engn Mashhad Iran Ferdowsi Univ Mashhad Dept Elect Engn Mashhad Iran Deakin Univ Dept Elect Engn Sch Engn Geelong Vic 3217 Australia

An important problem in designing RFIC in CMOS technology is the parasitic elements of passive and active devices that complicate design calculations. This article presents three LNA topologies including cascode, folded cascade, and differential cascode and then introduces image rejection filters for low-side and high-side injection. Then, a new method for design and optimization of the circuits based on a Pareto-based multiobjective genetic algorithm is proposed. A set of optimum device values and dimensions that best match design specifications are obtained. The optimization method is layout aware, parasitic aware, and simulation based. Circuit simulations are carried out based on TSMC 0.18 mu m CMOS technology by using Hspice. (C) 2010 Wiley Periodicals, Inc Int J RF and Microwave CAE 20 286-297, 2010

关键词： image rejection filter LNA multiobjective genetic algorithm optimization

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