Self-Tuning Controllers Based on Adaptive Fuzzy Clustering

In reverse engineering one often needs to generate geometrical models of old mechanical parts. Manual redrafting of old products using computer aided design (CAD) software is a laborious procedure. To overcome this problem, an automated procedure is proposed. The proposed approach is based on using clustering to segment the range image of the object and then constructing a solid model using an automated procedure. To demonstrate the feasibility of the proposed method, the model generation for convex planar faceted objects is considered. A generalized version of the adaptive fuzzy c-elliptotype clustering algorithm suitable for higher dimensional data is used to identify the planar facets in noisy range images when the number of facets is unknown.

Statistical as well as adaptive clustering approaches are being currently used for both segmentation and vector quantization of medical images. However, a comparative evaluation of both approaches has rarely been done to identify the efficacy of such approaches to specific applications, for example, image segmentation and vector quantization. The rate distortion functions of three clustering algorithms, namely, the statistical based deterministic annealing, the adaptive fuzzy leader clustering algorithm, and LBG, have been computed for vector quantization using multi-scale vectors in the wavelet domain. Such comparative evaluation serves as a guide for proper selection of clustering algorithms for global codebook generation in vector quantization and for image segmentation.

The silicon content of molten iron is a key index to reveal the thermal state in the blast furnace (BF). To improve the quality of molten iron, it is important to obtain silicon content online accurately. However, owing to the harsh environment in BF hearth, it is difficult to realize the online measurement of the silicon content of molten iron. To handle this problem, a novel soft sensoring method based on adaptive stacked polymorphic model is proposed. First, an adaptive uncertain fuzzy clustering algorithm (AUFC) is utilized to obtain the distribution characteristics and reduce the complexity of the process data, thereby the training samples are divided into several sub-classes. Second, the adaptive weight echo state network (AW-ESN) and time difference AW-ESN (TD-AWESN) are proposed to establish polymorphic models. Inspired by the stacking strategy, sub-models based on stacked polymorphic model are designed, which can effectively solve the time-varying and the strong nonlinear problems in the BF ironmaking process. Third, to avoid model degradation caused by the changes of working conditions, an ensemble strategy is adopted to combine the predicted results of multiple sub-models. The experimental results in an industrial BF show that the soft sensor based on an adaptive polymorphic model can reach the required prediction accuracy and provide reliable silicon content.

This paper addresses the problem of incremental and on-line learning of indoor dynamic environments by mobile robots. The proposed method further improves the Enhanced Adaptive Fuzzy Clustering (EAFC) algorithm for segment detection by using probabilistic techniques. In this study, the environment boundaries is extracted by the AFC algorithm and the probabilistic technique is used to estimate and update the state of dynamic objects in the mobile robot workplace. The method has been implemented and tested in a Pioneer II mobile robot.

Cluster analysis is an unsupervised most important research topics in the field of pattern recognition. Fuzzy clustering from the sample to the category of uncertainty description, it is possible to more objectively reflect the real world. Traditional fuzzy clustering algorithm can not achieve the optimal allocation of the number of clusters is calculated automatically. In this paper, by adopting the idea of hierarchical clustering, one can automatically and efficiently determine the optimal number of clusters of new adaptive fuzzy c-means clustering algorithm-A-FCM algorithm. Numerical experiments show that the other through a variety of validity function to determine the number of clusters of adaptive fuzzy clustering algorithm, the better the performance of the method.

Many components of intelligent transportation systems require different levels of the traffic flow forecasting. This paper presents a novel short-term traffic flow forecasting model using a distributed radial basis function neural network (RBFNN) based on adaptive fuzzy c-means (FCM) clustering algorithm. FCM clustering algorithm is used to classify training objects into a couple of clusters, each cluster is trained by a sub RBFNN, and membership values are used for combining several RBFNN outputs to obtain the final result. In the online stage, the membership values are computed using an adaptive fuzzy clustering algorithm for the new object. The real traffic data are used to demonstrate the effectiveness of the method.

Mining event logs for knowledge discovery based on adaptive efficient fuzzy Kohonen clustering network

Mapping adaptive fuzzy Kohonen clustering network onto distributed image processing system

Software components analysis is a critical technique for software development and maintenance. Clustering techniques have been widely used in grouping related software components. However, software is complex, but clustering techniques used in software engineering typically adopt only one metric to measure the similarity of components. This paper proposes an adaptive fuzzy clustering technique based on possibilistic clustering algorithms to address the issue of single metric. The proposed technique collaboratively considers distance, density, and the trend of density change of component instances in the membership degree calculation. The post clustering separation of clustered components based on the predefined thresholds and regrouping of the separated component points result in higher cohesive clustering. The proposed algorithm has been evaluated via experiments using a network protocol RSVP-TE system. The comparison of Hierarchical, Self-organizing map (SOM), and fuzzy c-means (FCM) against adaptive fuzzy clustering proposed in this paper indicates that the adaptive fuzzy clustering group software component instances into more cohesive clusters while it is also insensitive to parameter settings.

To eliminate the effect of the uncertain disturbances and improve the control accuracy of spacecraft Attitude Control System, a nonlinear control algorithm named nonsingular terminal sliding-mode feedback controller is proposed in this work, which is mainly made up of nonsingular terminal sliding-mode controller and sliding-mode feedback controller. In the first place, nonsingular terminal sliding-mode controller is designed, which guarantees global asymptotic convergence of the attitude in the presence of the uncertain perturbations from the space. Despite that, it is the influence of the uncertain disturbances that hinder the control accuracy. Then, in order to promote the control accuracy, the sliding-mode feedback controller based on the principle of minimum sliding-mode error is proposed, which is used to compensate the control errors of the nonsingular terminal sliding-mode controller caused by the uncertainties. Hence, the determination principle of the weighting matrix in sliding-mode feedback controller is discussed, and the algorithm structure of the sliding-mode feedback controller is also analyzed, which provides the theoretical basis for the sliding-mode feedback controller. By contrast, an adaptive fuzzy algorithm is designed and introduced into the nonsingular terminal sliding-mode controller to improve the control accuracy, which named the nonsingular terminal fuzzy sliding-mode controller. Last but not the least, several numerical examples are presented to demonstrate the efficacy of the proposed nonsingular terminal sliding-mode feedback controller. Simulation results confirm that the control accuracy of the nonsingular terminal sliding-mode feedback controller is higher than the nonsingular terminal sliding-mode controller and the same as nonsingular terminal fuzzy sliding-mode controller. Not only is the calculation of the nonsingular terminal fuzzy sliding-mode feedback controller smaller than nonsingular terminal fuzzy sliding-mode controller, the adjusted parameters are also fewer than nonsingular terminal fuzzy sliding-mode controller obviously. The numerical results clearly indicate that the proposed nonsingular terminal sliding-mode feedback controller based on the principle of minimum sliding-mode error can compensate control errors accurately and quickly; therefore, it can reduce the effect of the uncertainties from the space indirectly.

The designs of solar energy systems depend mainly on the solar radiation that reaches the earth's surface, as it is difficult to determine the amount of solar radiation precisely due to several climatic, geographical and temporal factors. Therefore, forecasting of solar radiation is necessary before using solar energy systems. In this paper, the researchers present an accuracy MAMDANI fuzzy inference system for solar radiation prediction with meteorological data. This system is based with a two-stage method for Fuzzy Rules Learning through Clustering (FRLC). In the first stage, the subtractive clustering is used to extract the fuzzy rules, the second stage is a linguistic approximation and a refinement of the learned solutions with linguistic hedges. FRLC is compared to multilayer feed-forward neural network and support vector regression. The results of the experiments show the efficacy of linguistic fuzzy rules in the forecasting of solar radiation. In parallel with the prediction, the model provides a good balance between interpretability and accuracy.

The greenhouse environment is a complex nonlinear time-varying system. It is difficult to establish accurate mathematical model. Adaptive fuzzy control does not rely on the mathematical model of the controlled object, and has the advantages of adaptive learning algorithm. The learning algorithm is to rely on the data information to adjust the parameters of fuzzy logic system. So we can adopt direct adaptive fuzzy control to control the greenhouse environment temperature. Through the MATLAB simulation, we simulate the control effect of fuzzy controller in different working conditions. The results show that the fuzzy adaptive controller can meet the requirements of the greenhouse environment temperature control.

Wireless sensor networks (WSNs) consist of a large number of small, low data rate and inexpensive nodes that communicate in order to sense or control a physical phenomenon. The major difference between the WSN and the traditional wireless network is that sensors are very sensitive to energy consumption. Moreover, the performance of the sensor network applications highly depends on the lifetime of the network and we expect the lifetime of several months to several years. Thus, energy saving is crucial in designing long-lived wireless sensor networks. Many researchers have focused on developing energy efficient cluster based protocols for WSNs, but there has not been much research on event driven WSNs and, their focus is on continuous driven networks. In this paper, we propose a modified algorithm of Low Energy Adaptive Clustering Hierarchy (LEACH) protocol which is a well known energy efficient clustering algorithm for WSNs. Our modified protocol called “Adaptive and Energy Efficient Clustering Algorithm for Event-Driven Application in Wireless Sensor Networks (AEEC)” is aimed at prolonging the lifetime of a sensor network by balancing energy usage of the nodes. AEEC makes the nodes with more residual energy have more chances to be selected as cluster head. Also, we use elector nodes which take the responsibility of collecting energy information of the nearest sensor nodes and selecting the cluster head. We compared the performance of our AEEC algorithm with the LEACH protocol using simulations .

Adaptive gravitational clustering algorithm integrated with noise detection

The objective of this study is to enhance the responsiveness and precision of control systems through the acceleration of evolutionary programming in the optimization of nonlinear fuzzy control systems. Nonlinear systems are ubiquitous in engineering practice, presenting significant challenges due to their complexity and uncertainty for traditional control methodologies. Fuzzy control, an intelligent control approach rooted in empirical rules, possesses the capability to navigate uncertain and nonlinear issues. However, conventional fuzzy controllers struggle to achieve optimal control performance when confronted with highly complex nonlinear systems. Consequently, this paper introduces the application of accelerated evolutionary programming (RGA) for the optimization of fuzzy controllers, aiming to augment their performance in nonlinear systems. By merging genetic algorithms with a fuzzy adaptive PID controller, the optimized fuzzy controller is better equipped to adapt to the dynamic changes of nonlinear systems, offering robust and efficient control strategies. The results of simulation experiments demonstrate a marked improvement in response time, stability, and error reduction with the fuzzy control system based on RGA optimization. This study offers novel insights and methodologies for the further optimization of control within nonlinear systems, while providing theoretical support for the practical applications of complex systems.