Hybrid Computational Intelligence Schemes in Complex Domains: An Extended Review
Abstract
The increased popularity of hybrid intelligent systems in recent times lies to the extensive success of these systems in many real-world complex problems. The main reason for this success seems to be the synergy derived by the computational intelligent components, such as machine learning, fuzzy logic, neural networks and genetic algorithms. Each of these methodologies provides hybrid systems with complementary reasoning and searching methods that allow the use of domain knowledge and empirical data to solve complex problems. In this paper, we briefly present most of those computational intelligent combinations focusing in the development of intelligent systems for the handling of problems in real-world applications.
... Bu başarının ana nedeni makine öğrenmesi, bulanık mantık, yapay sinir ağları ve genetik algoritmalar gibi sayısal zeki bileşenlerden elde edilen sinerjidir. Bu yöntemlerden her biri, karmaşık problemi çözmek için bilgi alanını ve gözlemsel veriyi kullanmaya izin veren tamamlayıcı akıl yürütme ve arama yöntemleri ile melez sistemler oluşturulmasını sağlarlar (Tsakonas and Dounias, 2002). Melez sistem modelleri şu şekilde sınıflandırılırlar (Abraham and Nath, 2000) : a) Bağımsız mimari: Bu mimaride bileşenler birbirileri ile etkileşmezler. ...
Bu tezde, günlük hayatta karşılaşılan karmaşık karar problemlerinin optimal olarak çözülebilmesi için melez zeki karar destek sistemlerinin geliştirilmesi ve uygulanması konusu araştırılmıştır. Karar problemleri genel olarak birden fazla kriter içerir ve çok kriterli karar verme (ÇKKV) algoritmaları kullanılarak çözülebilmektedir fakat bu problemler çoğu zaman belirsiz ve doğrusal olmayan özellikler içermektedir. Bu tür belirsizlik içeren durumlarda doğrusal olmayan özelliklerin modellenmesi için ÇKKV yöntemleri yetersiz kalmakta ve bulanık mantık, bulanık çıkarım ve yapay sinir ağları gibi yapay zeka tekniklerine ihtiyaç duyulmaktadır. Karar destek sistemleri (KDS) ise belirsizlik seviyesi yüksek olan bu tür karar problemlerini çözmek için analitik modeller kullanarak karar vericiye kolaylık sağlarlar.Çalışmada melez zeki karar destek sistemlerinin tasarımı ve gerçekleştirimi için genel bir karar destek modeli önerilmiştir. İki farklı karmaşık karar problemi için ÇKKV ile yapay zeka tekniklerinin melez olarak birlikte kullanımı incelenmiş ve problemlerin çözümüne yönelik karar destek sistemleri geliştirilmiştir. Daha sonra geliştirilen melez zeki karar destek sistemlerinin gerçekleştirimi yapılmıştır. Sistem mimarileri ve deneysel sonuçlar örnekler üzerinde açıklanmıştır. Geliştirilen karar destek sistemlerinin iyileştirilmesi için yapılabilecekler ve diğer karar problemlerine nasıl uyarlanabileceği tartışılmıştır.
ENGLISH:
In this thesis, developing and performing hybrid intelligent decision support systems has been studied to obtain optimal solution for the complex decision problems in daily life. Decision problems usually include more than one criterion and they can be solved using multi criteria decision making (MCDM) algorithms, however, these problems usually contain non-linear and uncertain attributes. In such undetermined cases, MCDM techniques are insufficient to model non-linear attributes therefore artificial intelligence techniques such as fuzzy logic, fuzzy inference and artificial neural networks are required. Decision Support Systems (DSS) provide simplicity using analytical models for decision makers to solve decision problems which have high uncertainty level.In this study; a general decision support model has been proposed to design and implement hybrid intelligent decision support systems. Usage of MCDM and artificial intelligence techniques together has been examined for two different decision problems and decision support systems have been developed to solve these problems. Then implementation of developed hybrid intelligent decision support systems has been performed. System architectures and experimental results with illustrative examples are demonstrated. There is some important discussion on how the developed decision support systems can be improved and how they can be applied to other decision problems.
... These prime results presented in the paper, are indicative of the usability of the SVMs, denoting the competitiveness of this approach among other intelligent approaches for data driven forecasting and decision making. Further research in this domain, may involve hybrid computational intelligent schemes (see a detailed review in [9], for details), while those approaches have been proved in many cases capable of capturing nearly stochastic or chaotic processes offering a high classification and prediction rate. ...
A soft computing method for result prediction of football games based on machine learning techniques such
as support vector machines is proposed in the article. The model is taking into account the following features of
football teams: difference of infirmity factors; difference of dynamics profile; difference of ranks; host factor;
personal score of the teams. Testing shows that the proposed model achieves a satisfactory estimation of the
actual game outcomes. The current work concludes with the recommendation of support vector machines
technique as a powerful approach, for the creation of result prediction models of diverse sport championships.
... Knight et al. [10] demonstrated methods to generate irradiance data based on hour is known as GEN which involves the contribution of the month based standard irradiance data to produce hour based irradiance figures [10]. The hypothesis that simulations run with those produced data for a distinct one year time would provide fairly parallel outcomes to those gained from simulations determined networks, and 4. evolutionary computing and genetic algorithms [25]. The initial hybrid approach composed of fuzzy rule bases system and machine learning techniques can be found in [26]. ...
This paper empirically demonstrates that a hybrid or aggregated machine learning using Mamdani type fuzzy inference system (FIS) for solar power prediction can deliver improved prediction accuracy that outperforms those of single machine learning technique. However, for aggregation to be effective, the individual machine learning techniques must be as accurate as possible. Therefore, this paper is presented in two major phases. In the first phase, the selection procedure of those individual machine learning techniques and the adopted strategies to make them as accurate as possible are briefly explained. According to the experimental results the most potential three machine learning techniques namely Least Median Square (LMS), Multilayer Perceptron (MLP) and Support Vector Machine (SVM) were selected for the components of the hybrid prediction method. After applying each of the improvement strategies on the machine learning techniques, six hours ahead solar power predictions were performed and justified with the error validation metrics. The prediction outcome of each and every step was compared with the prediction outcome of the previous steps to ensure the continual improvement of the prediction accuracy of those individuals. All the prediction accuracy results executed by the individuals in the first phase are summarized which clearly reveals a significant contribution of this paper. It is found from the summarized results that those individual machine learning techniques reach at the best of their accuracy level by the combined effect of applying feature selection and parameter optimization on them. In the second phase, the best possible individual accurate predictions from LMS, MLP and SVM are passed as inputs towards the Mamdani FIS to accomplish the task to non-linearly integrate them. From the empirical results it is obvious that the final aggregated prediction delivers better accuracy than any of the individuals in terms of the error validation metrics Mean Absolute Error (MAE), Mean Absolute Percentage Error
... At present, the most common used optimization techniques include genetic algorithm, simulated annealing, particle swarm optimization, tabu search and grid search [16]. Many evidences demonstrate the hybrid computational intelligence performs superior or in a competitive way to simple standard intelligent techniques due to the global search [17]. Since the objective of cost-sensitive classification can be described as minimizing the total misclassification cost [18], it is ideal to build the classifier with the aim to achieve the lowest value of the total misclassification cost. ...
Cost-sensitive learning is an important topic in bankruptcy prediction concerning the unequal misclassification cost of different classes. Learning vector quantization (LVQ) is a powerful tool to solve bankruptcy prediction problem as a classification task. The heuristic algorithms are applied widely in conjunction with artificial intelligent methods for solving optimization problems. The hybridization of heuristic techniques with existing classification algorithms is well illustrated in the field of bankruptcy prediction. In this paper, three hybrid heuristic-based LVQ approaches which combine LVQ with genetic algorithm, simulated annealing and particle swarm optimization respectively, are proposed to minimize the total misclassified cost under the asymmetric cost preference. The idea behind the hybrid classifier is the adoption of heuristic algorithms for the determination of the connection weights of the LVQ network. Experiments on French private company data show the proposed approaches offer interesting and viable alternatives for predictive reinforcement in cost-sensitive context. Index Terms—bankruptcy prediction, learning vector quantization, heuristic algorithm, asymmetric misclassification cost, cost-sensitive learning, expected misclassified cost.
The evolution of communication networks and information systems, to support wireless access, cloud and grid computing, and big data, provides great business opportunities. However, it also generates a new trend of sophisticated network threats and offers several challenges in securing information and systems confidentiality, integrity and availability. The traditional techniques used by security experts are mostly static and lack the much needed characteristics of adaptation and self-organization, computational efficiency and error resilience to deal with evolving attacks. The inherent characteristics of computational intelligence (CI) paradigms provide a promising alternative that has gained popularity resulting in significant applications in information security. There is a plethora of CI paradigms commonly used in this domain including artificial neural networks, evolutionary computing, fuzzy systems, and swarm intelligence. This chapter provides an overview of the widely-recognized CI paradigms and shades the light on some of their potential applications in information security.
Oil and gas metering is primarily used as the basis for evaluating the economic viability of oil wells. Owing to the economic implications of oil and gas metering, the subject of oil and gas flow rate measurement has witnessed a sustained interest by the oil and gas community and the academia. To the best of the authors' knowledge, despite the growing number of published articles on this subject, there is yet no comprehensive critical review on it. The objective of this paper is to provide a broad overview of models and modelling techniques applied to the estimation of oil and gas flow rate through chokes while also critically evaluating them. For the sake of simplicity and ease of reference, the outcomes of the review are presented in tables in an integrated and concise manner. The articles for this review were extracted from many subject areas. For the theoretical pieces related to oil and gas flow rate in general, the authors relied heavily upon several key drilling fluid texts. For operational and field studies, the authors relied on conference proceedings from the society of petroleum engineers. These sources were supplemented with articles in peer reviewed journals in order to contextualize the subject in terms of current practices. This review is interspersed with critiques of the models while the areas requiring improvement were also outlined. Findings from the bibliometric analysis indicate that there is no universal model for all flow situations despite the huge efforts in this direction. Furthermore, a broad survey of literature on recent flow models reveals that researchers are gravitating towards the field of artificial intelligence due to the tremendous promises it offers. This review constitutes the first critical compilation on a broad range of models applied to predicting oil and gas flow rates through chokes.
The analytical evaluation of production system performance measures is a difficult task. Over the years, various methods have been developed to solve specific cases of very short production lines. However, formulae for estimating the mean production rate (throughput) are lacking. Recent developments in artificial intelligence simplify their use in the solution of symbolic regression problems. In this work, we use genetic programming (GP) to obtain approximate formulae for calculating the throughput of short reliable approximately balanced production lines, for which the processing times are exponentially distributed. A hybrid GP&GA scheme reduces the search space, in which GP uses genetic algorithms (GA) as a search engine. The scheme produces polynomial formulae for throughput estimation for the first time. To train the GP algorithm we use MARKOV, an accurate algorithm for calculating numerically the exact throughput of short exponential production lines. A few formulae, not previously reported in the literature, are presented. These formulae give close results to the exact results from the MARKOV algorithm, for short (up to five stations) reliable approximately balanced production lines without intermediate buffers. Also, the robustness of these formulae is satisfactory. In addition, the proposed hybrid GP&GA scheme is useful for design/production engineers to adjust the formulae to other ranges of the mean processing rates; the algorithms are quickly retrained to generate a new approximate formula.
In this chapter, computational intelligence and its major methodologies are introduced, and then hybrid intelligent systems are defined, and the most popular hybrid intelligent approaches are discussed. The increased popularity of hybrid intelligent systems during the last decade is the result of the extensive success of these systems in a wide range of real-world complex problems, but also has to do with the increased capabilities of computational technology. One of the reasons for this success has to do with the synergy derived by the computational intelligent components, such as machine learning, fuzzy logic, neural networks, genetic algorithms, or other intelligent algorithms and techniques. Each of the partial methodologies provides hybrid systems with complementary reasoning and searching methods that allow the use of domain knowledge and empirical data to solve complex problems. The chapter includes recent advances and new findings in the area of hybrid computational intelligence.
Computational intelligence (CI) techniques have positively impacted the petroleum reservoir characterization and modeling landscape. However, studies have showed that each CI technique has its strengths and weaknesses. Some of the techniques have the ability to handle datasets of high dimensionality and fast in execution, while others are limited in their ability to handle uncertainties, difficult to learn, and could not deal with datasets of high or low dimensionality. The “no free lunch” theorem also gives credence to this problem as it postulates that no technique or method can be applicable to all problems in all situations. A technique that worked well on a problem may not perform well in another problem domain just as a technique that was written off on one problem may be promising with another. There was the need for robust techniques that will make the best use of the strengths to overcome the weaknesses while producing the best results. The machine learning concepts of hybrid intelligent system (HIS) have been proposed to partly overcome this problem. In this review paper, the impact of HIS on the petroleum reservoir characterization process is enumerated, analyzed, and extensively discussed. It was concluded that HIS has huge potentials in the improvement of petroleum reservoir property predictions resulting in improved exploration, more efficient exploitation, increased production, and more effective management of energy resources. Lastly, a number of yet-to-be-explored hybrid possibilities were recommended.
Artificial Intelligence (AI) has been used and applied in different sectors, such as engineering, economic, medicine, military, marine, etc. AI has also been applied for modelling, identification, optimisation, prediction, forecasting, and control of complex systems. The main objective of this paper is to present an overview of AI techniques for modelling, prediction and forecasting of solar radiation data. Published literature works presented in this paper show the potential of AI as a design tool for prediction and forecasting of solar radiation data; additionally, they present the advantages of using AI-based prediction solar radiation data in isolated areas where there no instrument for the measurement of this data, especially the parameters related to photovoltaic (PV) systems. Solar radiation plays a very important factor in PV-system performance and sizing.
Knowledge-based systems simulate human behavior in problem solving situations. Special systems of this type are expert systems and control systems. Expert systems support human experts in diagnosis or planning tasks while control systems automate technical process control. For both, the acquisition of expert knowledge is the most essential phase when building the knowledge-based system. During this phase, a knowledge engineer has to extract the problem-solving knowledge from the expert and to implement it in the knowledge base which is one component of the system to be developed. There exists a variety of methods like interview techniques, intelligent rule editors, automatic text analysis, and machine learning algorithms to support the knowledge acquisition process. This paper describes generalized learning techniques which can be used to acquire automatically different kinds of knowledge. Special emphasis is given to uncertain knowledge and especially to fuzzy knowledge.
Advances in Computational Intelligence and Learning: Methods and Applications presents new developments and applications in the area of Computational Intelligence, which essentially describes methods and approaches that mimic biologically intelligent behavior in order to solve problems that have been difficult to solve by classical mathematics. Generally Fuzzy Technology, Artificial Neural Nets and Evolutionary Computing are considered to be such approaches.
The Editors have assembled new contributions in the areas of fuzzy sets, neural sets and machine learning, as well as combinations of them (so called hybrid methods) in the first part of the book. The second part of the book is dedicated to applications in the areas that are considered to be most relevant to Computational Intelligence.
The last few years have produced a remarkable expansion of research in machine learning. The field has gained an unprecedented popularity, several new areas have developed, and some previously established areas have gained new momentum. While symbolic methods, both empirical and knowledge intensive (in particular, inductive concept learning and explanation-based methods), continued to be exceedingly active (see Parts Two and Three of the book, respectively), subsymbolic approaches, especially neural networks, have experienced tremendous growth (Part Five). Unlike past efforts that concentrated on single learning strategies, the new trends have been to integrate different strategies and to develop cognitive learning architectures (Part Four). There has been an increasing interest in experimental comparisons of various methods, and in theoretical analyses of learning algorithms. Researchers have been sharing the same data sets and have applied their techniques to the same problems in order to understand the relative merits of different methods. Theoretical investigations have brought new insights into the complexity of learning processes (Part Six).
This paper presents a methodology for power plant fault diagnosis, which is based on inductive machine learning and fuzzy logic. Expert knowledge concerning normal and faulty operation is acquired via knowledge acquisition techniques. We aim toward the construction of a dynamically changing fuzzy rule-based system, able to diagnose plant anomalies. The proposed system, deals with two important malfunctions of the boiler's circuit, namely, tube leakage and fouling. We demonstrate our approach by experimenting with real world data taken from a specific thermal plant.
