IEEE Transactions on Systems Man and Cybernetics Part C-Applications and Re最新文献

{"title":"Correction to \"approach by localization and multiobjective evolutionary optimization for flexible job-shop scheduling problems\"","authors":"I. Kacem, S. Hammadi, P. Borne","doi":"10.1109/TSMCC.2002.804307","DOIUrl":"https://doi.org/10.1109/TSMCC.2002.804307","url":null,"abstract":"","PeriodicalId":55005,"journal":{"name":"IEEE Transactions on Systems Man and Cybernetics Part C-Applications and Re","volume":"31 1","pages":"172"},"PeriodicalIF":0.0,"publicationDate":"2002-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73532576","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Computational military tactical planning system","authors":"R. Kewley, M. Embrechts","doi":"10.1109/TSMCC.2002.801352","DOIUrl":"https://doi.org/10.1109/TSMCC.2002.801352","url":null,"abstract":"A computational system called fuzzy-genetic decision optimization combines two soft computing methods, genetic optimization and fuzzy ordinal preference, and a traditional hard computing method, stochastic system simulation, to tackle the difficult task of generating battle plans for military tactical forces. Planning for a tactical military battle is a complex, high-dimensional task which often bedevils experienced professionals. In fuzzy-genetic decision optimization, the military commander enters his battle outcome preferences into a user interface to generate a fuzzy ordinal preference model that scores his preference for any battle outcome. A genetic algorithm iteratively generates populations of battle plans for evaluation in a stochastic combat simulation. The fuzzy preference model converts the simulation results into a fitness value for each population member, allowing the genetic algorithm to generate the next population. Evolution continues until the system produces a final population of high-performance plans which achieve the commander's intent for the mission. Analysis of experimental results shows that co-evolution of friendly and enemy plans by competing genetic algorithms improves the performance of the planning system. If allowed to evolve long enough, the plans produced by automated algorithms had a significantly higher mean performance than those generated by experienced military experts.","PeriodicalId":55005,"journal":{"name":"IEEE Transactions on Systems Man and Cybernetics Part C-Applications and Re","volume":"15 1","pages":"161-171"},"PeriodicalIF":0.0,"publicationDate":"2002-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77192773","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Guest editorial special issue on fusion of soft computing and hard computing in industrial applications","authors":"S. Ovaska, H. Vanlandingham","doi":"10.1109/TSMCC.2002.801346","DOIUrl":"https://doi.org/10.1109/TSMCC.2002.801346","url":null,"abstract":"I T IS A true honor and a great pleasure for us to be able to present a Special Issue on Fusion of Soft Computing and Hard Computing in Industrial Applications in the IEEE TRANSACTIONS ONSYSTEMS, MAN, AND CYBERNETICS—PART C: APPLICATIONS ANDREVIEWS. The roots of this Special Issue can be traced back to the SMC-98 conference that was held in La Jolla, CA. One of the highlights of that conference was the panel discussion on “New Frontiers in Information/Intelligent Systems.” Dr. Lotfi A. Zadeh was the moderator of the panel, and the panelists were all world-class scholars in the field of soft computing (SC). While the discussion was certainly stimulating and provided inspiring visions, something was missing—the dominating and continuing role of conventional hard computing (HC) in developing successful products was not recognized at all—and that made us thinking of possible technical activities on the fusionof these two principal methodologies. In this 21st century, the engineering problems are becoming increasingly demanding. Thus, a constructive fusion of all possible methodologies is needed in developing innovative and competitive solutions. The primary goal of the fusion or symbiosis of SC and HC is to create computationally efficient, highly predictable, robust, and intelligent systems. This Special Issue contains a representative collection of application papers, where the methodological fusion aspect is in a major role. A kind of “sister product” of this special issue was the recent panel discussion on the same topic, which took place in the SMCia/01 workshop. Dr. David B. Fogel moderated the American–European–Asian panel in Blacksburg, VA. One of the conclusions of the panel was that the fusion of soft computing and hard computing is certainly needed—and is already widely used—in developing intelligent systems and industrial products. Also, soft computing is like mathematics or computer programming without immediate connections to specific applications; this sets new requirements for the engineering curricula. Our Special Issue contains nine papers authored by recognized international scholars. The papers were selected by a strict peer review from those 23 manuscripts that were received for consideration from ten countries. These papers show that the fusion of soft computing and hard computing can really be advantageous when developing intelligent systems for various applications. Unfortunately, due to space limitations, we had to leave out a few high-quality contributions.","PeriodicalId":55005,"journal":{"name":"IEEE Transactions on Systems Man and Cybernetics Part C-Applications and Re","volume":"76 1","pages":"69-71"},"PeriodicalIF":0.0,"publicationDate":"2002-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86261792","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fusion of soft computing and hard computing in industrial applications: an overview","authors":"S. Ovaska, H. Vanlandingham, A. Kamiya","doi":"10.1109/TSMCC.2002.801354","DOIUrl":"https://doi.org/10.1109/TSMCC.2002.801354","url":null,"abstract":"Soft computing (SC) is an emerging collection of methodologies which aims to exploit tolerance for imprecision, uncertainty, and partial truth to achieve robustness, tractability, and low total cost. It differs from conventional hard computing (HC) in the sense that, unlike hard computing, it is strongly based on intuition or subjectivity. Therefore, soft computing provides an attractive opportunity to represent the ambiguity in human thinking with real life uncertainty. Fuzzy logic (FL), neural networks (NN), and genetic algorithms (GA) are the core methodologies of soft computing. However, FL, NN, and GA should not be viewed as competing with each other, but synergistic and complementary instead. Considering the number of available journal and conference papers on various combinations of these three methods, it is easy to conclude that the fusion of individual soft computing methodologies has already been advantageous in numerous applications. On the other hand, hard computing solutions are usually more straightforward to analyze; their behavior and stability are more predictable; and, the computational burden of algorithms is typically either low or moderate. These characteristics. are particularly important in real-time applications. Thus, it is natural to see SC and HC as potentially complementary methodologies. Novel combinations of different methods are needed when developing high-performance, cost-effective, and safe products for the demanding global market. We present an overview of applications in which the fusion of soft computing and hard computing has provided innovative solutions for challenging real-world problems. A carefully selected list of references is considered with evaluative discussions and conclusions.","PeriodicalId":55005,"journal":{"name":"IEEE Transactions on Systems Man and Cybernetics Part C-Applications and Re","volume":"26 1","pages":"72-79"},"PeriodicalIF":0.0,"publicationDate":"2002-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78282251","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fusing hard and soft computing for fault management in telecommunications systems","authors":"Roy Sterritt, D. Bustard","doi":"10.1109/TSMCC.2002.801725","DOIUrl":"https://doi.org/10.1109/TSMCC.2002.801725","url":null,"abstract":"Global telecommunication systems are at the heart of the Internet revolution. To support Internet traffic they have built-in redundancy to ensure robustness and quality of service. This requires complex fault management. The traditional hard approach is to reduce the number of alarm events (symptoms) presented to the operating engineer through monitoring, filtering and masking. The goal of the soft approach is to automate the analysis fully so that the underlying fault is determined from the evidence available and presented to the engineer. This paper describes progress toward automated fault identification through a fusion between these soft and hard computing approaches.","PeriodicalId":55005,"journal":{"name":"IEEE Transactions on Systems Man and Cybernetics Part C-Applications and Re","volume":"27 1","pages":"92-98"},"PeriodicalIF":0.0,"publicationDate":"2002-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73757865","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fusion of hard and soft computing techniques in indirect, online tool wear monitoring","authors":"B. Sick","doi":"10.1109/TSMCC.2002.801347","DOIUrl":"https://doi.org/10.1109/TSMCC.2002.801347","url":null,"abstract":"Indirect, online tool wear monitoring is one of the most difficult tasks in the context of process monitoring for metal-cutting machining processes. Based on a continuous acquisition of certain process parameters (signals such as cutting forces or acoustic emission) with multi-sensor systems, it is possible to estimate or to classify certain wear parameters. However, despite of intensive scientific research during the past decades, the development of reliable and flexible tool wear monitoring systems is an ongoing attempt. This article introduces a new, hybrid technique for tool wear monitoring in turning which fuses a physical process model (hard computing) with a neural network model (soft computing). The physical model describes the influence of cutting conditions (such as tool geometry or work material) on measured force signals and it is used to normalize these force signals. The neural model establishes a relationship between the normalized force signals and the wear state of the tool. The advantages of this approach are demonstrated by means of experimental results. Moreover, it is shown that the consideration of process parameters, cutting conditions, and wear in one model (either physical or neural) is extremely difficult and that existing hybrid approaches are not adequate. The ideas presented in this article can be transferred to many other process monitoring tasks.","PeriodicalId":55005,"journal":{"name":"IEEE Transactions on Systems Man and Cybernetics Part C-Applications and Re","volume":"569 1","pages":"80-91"},"PeriodicalIF":0.0,"publicationDate":"2002-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74413107","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intelligent bounds on modeling uncertainty: applications to sliding mode control","authors":"G. Buckner","doi":"10.1109/TSMCC.2002.801350","DOIUrl":"https://doi.org/10.1109/TSMCC.2002.801350","url":null,"abstract":"Robust control techniques such as sliding mode control (SMC) require a dynamic model of the plant and bounds on modeling uncertainty to formulate control laws with guaranteed stability. Although techniques for modeling dynamic systems and estimating model parameters are well established, very few procedures exist for estimating uncertainty bounds. In the case of SMC design, a conservative global bound is usually chosen to ensure closed-loop stability over the entire operating space. The primary drawbacks of this conservative, \"hard computing\" approach are excessive control activity and reduced performance, particularly in regions of the operating space where the model is accurate. In this paper, a novel approach to estimating uncertainty bounds for dynamic systems is introduced. This \"soft computing\" approach uses a unique artificial neural network, the 2-Sigma network, to bound modeling uncertainty adaptively. This fusion of intelligent uncertainty bound estimation with traditional SMC results in a control algorithm that is both robust and adaptive. Simulations and experimental demonstrations conducted on a magnetic levitation system confirm these capabilities and reveal excellent tracking performance without excessive control activity.","PeriodicalId":55005,"journal":{"name":"IEEE Transactions on Systems Man and Cybernetics Part C-Applications and Re","volume":"35 1","pages":"113-124"},"PeriodicalIF":0.0,"publicationDate":"2002-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81485685","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adaptive dynamic programming","authors":"J. Murray, C. Cox, G. Lendaris, R. Saeks","doi":"10.1109/TSMCC.2002.801727","DOIUrl":"https://doi.org/10.1109/TSMCC.2002.801727","url":null,"abstract":"Unlike the many soft computing applications where it suffices to achieve a \"good approximation most of the time,\" a control system must be stable all of the time. As such, if one desires to learn a control law in real-time, a fusion of soft computing techniques to learn the appropriate control law with hard computing techniques to maintain the stability constraint and guarantee convergence is required. The objective of the paper is to describe an adaptive dynamic programming algorithm (ADPA) which fuses soft computing techniques to learn the optimal cost (or return) functional for a stabilizable nonlinear system with unknown dynamics and hard computing techniques to verify the stability and convergence of the algorithm. Specifically, the algorithm is initialized with a (stabilizing) cost functional and the system is run with the corresponding control law (defined by the Hamilton-Jacobi-Bellman equation), with the resultant state trajectories used to update the cost functional in a soft computing mode. Hard computing techniques are then used to show that this process is globally convergent with stepwise stability to the optimal cost functional/control law pair for an (unknown) input affine system with an input quadratic performance measure (modulo the appropriate technical conditions). Three specific implementations of the ADPA are developed for 1) the linear case, 2) for the nonlinear case using a locally quadratic approximation to the cost functional, and 3) the nonlinear case using a radial basis function approximation of the cost functional; illustrated by applications to flight control.","PeriodicalId":55005,"journal":{"name":"IEEE Transactions on Systems Man and Cybernetics Part C-Applications and Re","volume":"11 1","pages":"140-153"},"PeriodicalIF":0.0,"publicationDate":"2002-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91260647","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Performance comparison of fused soft control/hard observer type controller with hard control/hard observer type controller for switched reluctance motors","authors":"Chunming Shi, A. Cheok","doi":"10.1109/TSMCC.2002.801724","DOIUrl":"https://doi.org/10.1109/TSMCC.2002.801724","url":null,"abstract":"Both soft computing (SC) and hard computing (HC) techniques are often successful for solving real-world control problems. In cases where problems could be solved by either or both methodologies, an important research problem is to find what are the advantages for fusing SC methods together with HC methods, rather than using the HC method alone. Hence, in this paper, a performance comparison is detailed for a fused soft control/hard observer type controller (where a classical or HC type observer is fused with an adaptive fuzzy or SC type controller) and a hard control/hard observer type controller (where both the observer and feedback linearization controller are classical HC types). The domain in which this comparison is made is for the sensorless speed control of switched reluctance motors (SRMs). This is because this type of motor has highly nonlinear characteristics, and the HC type controller can often be detrimentally affected by modeling inaccuracies, as well as noise. Simulation and experimental results are illustrated to show the performance comparison of the soft control/hard observer type controller and the hard control/hard observer type controller under a wide range of identical operation conditions including transient speed and torque, SRM model parameter variations, and measurement noise. It can be seen from the results that the soft control/hard observer type exhibits a better performance than the hard control/hard observer type controller.","PeriodicalId":55005,"journal":{"name":"IEEE Transactions on Systems Man and Cybernetics Part C-Applications and Re","volume":"9 1","pages":"99-112"},"PeriodicalIF":0.0,"publicationDate":"2002-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79177377","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Incorporating soft computing techniques into a probabilistic intrusion detection system","authors":"Sung-Bae Cho","doi":"10.1109/TSMCC.2002.801356","DOIUrl":"https://doi.org/10.1109/TSMCC.2002.801356","url":null,"abstract":"There are a lot of industrial applications that can be solved competitively by hard computing, while still requiring the tolerance for imprecision and uncertainty that can be exploited by soft computing. This paper presents a novel intrusion detection system (IDS) that models normal behaviors with hidden Markov models (HMM) and attempts to detect intrusions by noting significant deviations from the models. Among several soft computing techniques neural network and fuzzy logic are incorporated into the system to achieve robustness and flexibility. The self-organizing map (SOM) determines the optimal measures of audit data and reduces them into appropriate size for efficient modeling by HMM. Based on several models with different measures, fuzzy logic makes the final decision of whether current behavior is abnormal or not. Experimental results with some real audit data show that the proposed fusion produces a viable intrusion detection system. Fuzzy rules that utilize the models based on the measures of system call, file access, and the combination of them produce more reliable performance.","PeriodicalId":55005,"journal":{"name":"IEEE Transactions on Systems Man and Cybernetics Part C-Applications and Re","volume":"12 1","pages":"154-160"},"PeriodicalIF":0.0,"publicationDate":"2002-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86146431","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}