Abstract: We vary the way an individual in the particle swarm interacts with its neighbors. Performance depends on population topology as well as algorithm version.

Abstract: This paper presents a comparative study of the predictive performances of neural network time series models for forecasting failures and reliability in engine systems. Traditionally, failure data analysis requires specifications of parametric failure distributions and justifications of certain assumptions, which are at times difficult to validate. On the other hand, the time series modeling technique using neural networks provides a promising alternative. Neural network modeling via feed-forward multilayer perceptron (MLP) suffers from local minima problems and long computation time. The radial basis function (RBF) neural network architecture is found to be a viable alternative due to its shorter training time. Illustrative examples using reliability testing and field data showed that the proposed model results in comparable or better predictive performance than traditional MLP model and the linear benchmark based on Box–Jenkins autoregressive-integrated-moving average (ARIMA) models. The effects of input window size and hidden layer nodes are further investigated. Appropriate design topologies can be determined via sensitivity analysis.

Abstract: The automatic design of fuzzy rule-based classification systems based on labeled data is considered. It is recognized that both classification performance and interpretability are of major importance and effort is made to keep the resulting rule bases small and comprehensible. For this purpose, an iterative approach for developing fuzzy classifiers is proposed. The initial model is derived from the data and subsequently, feature selection and rule-base simplification are applied to reduce the model, while a genetic algorithm is used for parameter optimization. An application to the Wine data classification problem is shown.

Abstract: This paper deals with the approximation behaviour of soft computing techniques. First, we give a survey of the results of universal approximation theorems achieved so far in various soft computing areas, mainly in fuzzy control and neural networks. We point out that these techniques have common approximation behaviour in the sense that an arbitrary function of a certain set of functions (usually the set of continuous function, C) can be approximated with arbitrary accuracy ? on a compact domain. The drawback of these results is that one needs unbounded numbers of “building blocks” (i.e. fuzzy sets or hidden neurons) to achieve the prescribed ? accuracy. If the number of building blocks is restricted, it is proved for some fuzzy systems that the universal approximation property is lost, moreover, the set of controllers with bounded number of rules is nowhere dense in the set of continuous functions. Therefore it is reasonable to make a trade-off between accuracy and the number of the building blocks, by determining the functional relationship between them. We survey this topic by showing the results achieved so far, and its inherent limitations. We point out that approximation rates, or constructive proofs can only be given if some characteristic of smoothness is known about the approximated function.

Abstract: In this paper, we describe a new application domain for intelligent autonomous systems--intelligent buildings (IB). In doing so we present a novel approach to the implementation of IB agents based on a hierarchical fuzzy genetic multi-embedded-agent architecture comprising a low-level behaviour based reactive layer whose outputs are co-ordinated in a fuzzy way according to deliberative plans. The fuzzy rules related to the room resident comfort are learnt and adapted online using our patented fuzzy-genetic techniques (British patent 99-10539.7). The learnt rule base is updated and adapted via an iterative machine-user dialogue. This learning starts from the best stored rule set in the agent memory (Experience Bank) thereby decreasing the learning time and creating an intelligent agent with memory. We discuss the role of learning in building control systems, and we explain the importance of acquiring information from sensors, rather than relying on pre-programmed models, to determine user needs. We describe how our architecture, consisting of distributed embedded agents, utilises sensory information to learn to perform tasks related to user comfort, energy conservation, and safety. We show how these agents, employing a behaviour-based approach derived from robotics research, are able to continuously learn and adapt to individuals within a building, whilst always providing a fast, safe response to any situation. In addition we show that our system learns similar rules to other offline supervised methods but that our system has the additional capability to rapidly learn and optimise the learnt rule base. Applications of this system include personal support (e.g. increasing independence and quality of life for older people), energy efficiency in commercial buildings or living-area control systems for space vehicles and planetary habitation modules.

Abstract: Many methods for solving optimal control problems, whether direct or indirect, rely upon gradient information and therefore may converge to a local optimum. Global optimisation methods like Evolutionary algorithms, overcome this problem. In this work it is investigated how well novel and easy to understand Evolutionary algorithms, referred to as Differential Evolution (DE) algorithms, and claimed to be very efficient when they are applied to solve static optimisation problems, perform on solving multimodal optimal control problems. The results show that within the class of evolutionary methods, Differential Evolution algorithms are very robust, effective and highly efficient in solving the studied class of optimal control problems. Thus, they are able of mitigating the drawback of long computation times commonly associated with Evolutionary algorithms. Furthermore, in locating the global optimum these Evolutionary algorithms present some advantages over the Iterative Dynamic Programming (IDP) algorithm, which is an alternative global optimisation approach for solving optimal control problems. At present little knowledge is available to the selection of the algorithm parameters in the DE algorithm when they are applied to solve optimal control problems. Our study provides guidelines for this selection. In contrast to the IDP algorithm the DE algorithms have only a few algorithm parameters that are easily determined such that multimodal optimal control problems are solved effectively and efficiently.

Abstract: We study the problem of decomposing a Boolean function into a set of functions with fewer arguments. This problem has considerable practical importance in VLSI, for example, for designs using field-programmable gate arrays. The decomposition problem is old, and well understood when the function to be decomposed is specified by a truth table, or has one output only. However, modern design tools handle functions with many outputs and represent them by cubes, for reasons of efficiency. We develop a comprehensive theory of serial decompositions for multiple-output, partially specified, Boolean functions represented by cubes. A function f (x1 , . . . , xn) has a serial decomposition if it can be expressed as h(u1 , . . . , ur, g(v1 , . . . , vs)), where U = {u1 , . . . , ur} and V = {v1 , . . . , vs} are subsets of the set X = {x1 , . . . , xn} of input variables, and g and h have fewer input variables than f. The theory uses generalized set systems (which, in turn, are generalized partitions), which we call blankets.

Abstract: A methodology for the encoding of the chromosome of a genetic algorithm (GA) is described in the paper. The encoding procedure is applied to the problem of automatically generating fuzzy rule-based models from data. Models generated by this approach have much of the flexibility of black-box methods, such as neural networks. In addition, they implicitly express information about the process being modelled through the linguistic terms associated with the rules. They can be applied to problems that are too complex to model in a first principles sense and can reduce the computational overhead when compared to established first principles based models. The encoding mechanism allows the rule base structure and parameters of the fuzzy model to be estimated simultaneously from data. The principle advantage is the preservation of the linguistic concept without the need to consider the entire rule base. The GA searches for the optimum solution given a comparatively small number of rules compared to all possible. This minimises the computational demand of the model generation and allows problems with realistic dimensions to be considered. A further feature is that the rules are extracted from the data without the need to establish any information about the model structure a priori. The implementation of the algorithm is described and the approach is applied to the modelling of components of heating ventilating and air-conditioning systems.

Abstract: A novel model reference adaptive robust fuzzy control algorithm is presented for ship steering autopilot, which is an uncertain nonlinear system In the proposed algorithm, fuzzy logic systems have been used to approximate lumped unknown function in the ship steering systems and the adaptive mechanism with minimal learning parameter, ie only one parameter, has been achieved by use of Lyapunov approach The proposed methodology is verified using the simulation mode of the Dalian Maritime University's ocean-going training ship named Yulong It is shown that the proposed algorithm guarantees that the ship steering autopilot system is asymptotically stable and its tracking error can approach to zero

Abstract: We describe in this paper a hybrid method for adaptive model-based control of nonlinear dynamic systems using neural networks, fuzzy logic and fractal theory. The new neuro-fuzzy-fractal method combines soft computing techniques with the concept of the fractal dimension for the domain of nonlinear dynamic system control. The new method for adaptive model-based control has been implemented as a computer program to show that the neuro-fuzzy-fractal approach is a good alternative for controlling nonlinear dynamic systems. It is well known that chaotic and unstable behavior may occur for nonlinear systems. Normally, we will need to control this type of behavior to avoid structural problems with the system. We illustrate in this paper our new methodology with the case of controlling aircraft dynamic systems. For this case, we use mathematical models for the simulation of aircraft dynamics during flight. The goal of constructing these models is to capture the dynamics of the aircraft, so as to have a way of controlling this dynamics to avoid dangerous behavior of the aircraft dynamic system.

Abstract: We describe in this paper a new method for adaptive model-based control of robotic dynamic systems using a new hybrid fuzzy-neural approach. Intelligent control of robotic systems is a difficult problem because the dynamics of these systems is highly nonlinear. We describe an intelligent system for controlling robot manipulators to illustrate our fuzzy-neural hybrid approach for adaptive control. We use a new fuzzy inference system for reasoning with multiple differential equations for model selection based on the relevant parameters for the problem. In this case, the fractal dimension of a time series of measured values of the variables is used as a selection parameter. We use neural networks for identification and control of robotic dynamic systems. We also compare our hybrid fuzzy-neural approach with conventional fuzzy control to show the advantages of the proposed method for control.

Abstract: This paper presents the development of a remote-access control system which allows users to perform control experiments through Internet. A dc motor control module is used as an example to illustrate our design. The system is composed of an internal distributed system and an application system linked by a data acquisition (DAQ) interface card. Web server, video server and Laboratory Virtual Instrument Engineering Workbench (LabVIEW) controller server are designed based on a client-server structure. The experiment can be accessed from http://www.acae.cuhk.edu.hk/~accl/ibc/.

Abstract: The nature of a contradiction (conflict) between two belief functions is investigated Alternative ways of distributing the contradiction among nonempty subsets of frame of discernment are studied The paper employes a new approach to understanding contradictions and introduces an original notion of potential contradiction A method of an associative combination of generalized belief functions – minC combination and its derivation – is presented as part of the new approach A proportionalization of generalized results is suggested as well

Abstract: This paper proposes the use of the discrete wavelet transform (DWT) for the extraction of features from phonemes. Instead of using the short time Fourier transform for feature extraction a new set of features is obtained from the DWT. The new set of features overcomes the previously reported problem of shift variance in DWT based features. Training and test samples of the phonemes were obtained from the TIMIT database. To account for the fast changes in the phonemes, the features were calculated for different phoneme durations and the performance was compared. For the classification of the phonemes, two different classifiers were used, based on linear discriminant analysis and multi-layer perceptron.

Abstract: This paper proposes a new anytime possibilistic inference algorithm for min-based directed networks. Our algorithm departs from a direct adaptation of probabilistic propagation algorithms since it avoids the transformation of the initial network into a junction tree which is known to be a hard problem. The proposed algorithm is composed of several, local stabilization, procedures. Stabilization procedures aim to guarantee that local distributions defined on each node are coherent with respect to those of its parents. We provide experimental results which, for instance, compare our algorithm with the ones based on a direct adaptation of probabilistic propagation algorithms.

Abstract: The huge amount of data recorded by modern production systems definitely have the potential to provide information for product and process design, monitoring and control. This paper presents a soft-computing (SC)-based approach for the extraction of knowledge from the historical data of production. Since Self-Organizing Maps (SOM) provide compact representation of the data distribution, efficient process monitoring can be performed in the two-dimensional projection of the process variables. For the estimation of the product quality, multiple local linear models are identified, where the operating regimes of the local models are obtained by the Voronoi diagram of the prototype vectors of the SOM. The proposed approach is applied to the analysis of an industrial polyethylene plant. The detailed application study demonstrates that the SOM is very effective in the detection of the typical operating regions related to different product grades, and the model can be used to predict the product quality (melt index and density) based on measured process variables.

Abstract: Effective analysis of genome sequences and associated functional data requires access to many different kinds of biological information. A data warehouse [14,16] plays an important role for storage and analysis for genome sequence and functional data. A data warehouse stores lots of materialized views to provide an efficient decision-support or OLAP queries. The view-selection problem addresses to select a fittest set of materialized views from a variety of MVPPs 0 forms a challenge in data warehouse research. In this paper, we present genetic algorithm to choose materialized views. We also use experiments to demonstrate the power of our approach.

Abstract: This paper describes a new task-contracting schema for multi-agent manufacturing control based on soft computing. It aims to apply fuzzy techniques to implement a real-time multi-criteria task-contracting mechanism for part flow control in manufacturing floor. For comparison purposes, the paper also considers other recently proposed evolutionary strategies to adapt and optimize agents' decision parameters to the changing conditions of the manufacturing floor. All the considered approaches are compared on a detailed simulation model of a hypothetical manufacturing system that was recently proposed in literature as benchmark for multi-agent control systems.

Abstract: Laser tracking systems (LTS) are among the most accurate robot metrology systems. These laser interferometer systems combine the advantages of high resolution, large work space and contactless measurements. A target mounter on the end-effector of the robot, is automatically tracked by a laser beam targeted by the moving mirror. A prerequisite for utilizing a LTS to calibrate robots is that the LTS itself should be self-calibrated successfully. In this paper, we describe the experimental calibration of a PUMA 560 robot by using a LTS developed at the Robotics Center, Florida Atlantic University. The calibration is based on a prior successful self-calibration of the LTS. Calibration results are analyzed and confirmed using a verification method. This is the first actual calibration of robots using a LTS in the United States during the 20th centenary.

Abstract: During the data collecting and labeling process it is possible for noise to be introduced into a data set. As a result, the quality of the data set degrades and experiments and inferences derived from the data set become less reliable. In this paper we present an algorithm, called ANR (automatic noise reduction), as a filtering mechanism to identify and remove noisy data items whose classes have been mislabeled. The underlying mechanism behind ANR is based on a framework of multi-layer artificial neural networks. ANR assigns each data item a soft class label in the form of a class probability vector, which is initialized to the original class label and can be modified during training. When the noise level is reasonably small (< 30%), the non-noisy data is dominant in determining the network architecture and its output, and thus a mechanism for correcting mislabeled data can be provided by aligning class probability vector with the network output. With a learning procedure for class probability vector based on its difference from the network output, the probability of a mislabeled class gradually becomes smaller while that of the correct class becomes larger, which eventually causes a correction of mislabeled data after sufficient training. After training, those data items whose classes have been relabeled are then treated as noisy data and removed from the data set. We evaluate the performance of the ANR based on 12 data sets drawn from the UCI data repository. The results show that ANR is capable of identifying a significant portion of noisy data. An average increase in accuracy of 24.5% can be achieved at a noise level of 25% by using ANR as a training data filter for a nearest neighbor classifier, as compared to the one without using ANR.

Abstract: Rendering map data at scales smaller than their source can give rise to map displays exhibiting graphic conflict, such that objects are either too small to be seen or too close to each other to be distinguishable. Furthermore, scale reduction will often require important features to be exaggerated in size, sometimes leading to overlapping features. Cartographic map generalisation is the process by which any graphic conflict that arises during scaling is resolved. In this paper, we show how a Genetic Algorithm (GA) approach was used to resolve spatial conflict between objects after scaling, achieving near optimal solutions within practical time constraints.

Abstract: This paper is devoted to a general concept of openness and closedness with respect to arbitrary fuzzy relations – along with appropriate opening and closure operators. It is shown that the proposed framework unifies existing concepts, in particular, the one for fuzzy preorderings as well as the triangular norm-based approach to fuzzy mathematical morphology.

Abstract: In this paper, a new method is proposed for approximating a fuzzy relation on a finite universe by a min-transitive fuzzy relation that is `close' to it. The method consists of a cascade of T-transitive closure and opening operations, where the t-norm T gradually progresses from the Lukasiewicz t-norm W to the minimum operator M. The underlying T-transitive opening heuristic is particularly interesting for t-norms T that belong to the class of copulas.

Abstract: The use of the Internet for time-sensitive services, such as voice and video applications, requires a predictable quality of service. The TCP/IP differentiated services (Diff-Serv) architecture was introduced to achieve such performance. Network congestion control, however, still remains a critical and high priority issue. A number of researchers are looking at alternative schemes such as random early detection (RED) and its variants to handle congestion. In this paper we present the results of a fuzzy logic control approach to the implementation of RED --- Fuzzy-RED. We believe that with fuzzy logic we are able to use linguistic knowledge to implement better understood nonlinear probability discard functions, achieve better differentiation for packet discarding behaviors for aggregated flows, and so provide better quality of service to different kinds of traffic whilst maintaining high utilization.

Abstract: Troubleshooting is one of the areas where Bayesian networks are successfully applied [9] In this paper we show that the generally defined troubleshooting task is NP-hard We propose a heuristic function that exploits the conditional independence of all actions and questions given the fault of the device It can be used as a lower bound of the expected cost of repair in heuristic algorithms searching an optimal troubleshooting strategy In the paper we describe two such algorithms: the depth first search algorithm with pruning and the AO* algorithm

Abstract: Using the concept of selectors of random sets, we provide an interpretation for numerical degrees of possibility. The axioms (and hence the calculus of possibilities) of possibility measures are justified, in the context of random sets, on the basis that possibility distributions, as covering functions, lead to maxitive capacity functionals of random closed sets. Also, possibility measures appear as limits of probability measures in the study of large deviations principle, and as such, the idempotent operator max is justified. The problem of admissibility of possibility measures is also discussed.

Abstract: We introduce an operational way to reduce the spatial complexity in inference processes based on conditional lower–upper probabilities assessments. To reach such goal we must suitably exploit zero probabilities taking account of logical conditions characterizing locally strong coherence. We actually re-formulate for conditional lower–upper probabilities the notion of locally strong coherence already introduced for conditional precise probabilities. Thanks to the characterization, we avoid to build all atoms, so that several real problems become feasible. In fact, the real complexity problem is connected to the number of atoms. Since for an inferential process with lower–upper probabilities several sequences of constraints must be fulfilled, our simplification can have either a “global” or a “partial” effect, being applicable to all or just to some sequences. The whole procedure has been implemented by XLisp-Stat language. A comparison with other approaches will be done by an example.