This paper presents an efficient immune symbiotic evolution learning (ISEL) algorithm for the compensatory neuro-fuzzy controller (CNFC). The proposed ISEL method includes three major components-initial population, su...
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We abstract the bus transport networks(BTNs)to two kinds of complex networks with space L and spaceP methods *** improved community detecting algorithm(PKM agglomerative algorithm),we analyzethe community property of ...
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We abstract the bus transport networks(BTNs)to two kinds of complex networks with space L and spaceP methods *** improved community detecting algorithm(PKM agglomerative algorithm),we analyzethe community property of two kinds of BTNs *** results show that the BTNs graph described with space Lmethod have obvious community property,but the other kind of BTNs graph described with space P method have *** reason is that the BTNs graph described with space P method have the intense overlapping community propertyand general community division algorithms can not identify this kind of community *** overcome this problem,we propose a novel community structure called N-depth community and present a corresponding community detectingalgorithm,which can detect overlapping *** the novel community structure and detecting algorithmto a BTN evolution model described with space P,whose network property agrees well with real BTNs',we get obviouscommunity property.
In this paper Principal Components Analysis (PCA) is used for detecting faults in a plant with multiple operation modes. PCA reduces the dimensionality of the original historical data by projecting it onto a lower dim...
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In this paper Principal Components Analysis (PCA) is used for detecting faults in a simulated wastewater treatment plant (WWTP). Diagnosis tasks are treated using Fisher discriminant analysis (FDA). Both techniques ar...
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ISBN:
(纸本)9783902661548
In this paper Principal Components Analysis (PCA) is used for detecting faults in a simulated wastewater treatment plant (WWTP). Diagnosis tasks are treated using Fisher discriminant analysis (FDA). Both techniques are multivariate statistical techniques used in multivariate statistical process control (MSPC) and fault detection and isolation (FDI) perspectives. PCA reduces the dimensionality of the original historical data by projecting it onto a lower dimensionality space. It obtains the principal causes of variability in a process. If some of these causes change, it can be due to a fault in the process. FDA provides an optimal lower dimensional representation in terms of a discriminant between classes of data, where, in this context of fault diagnosis, each class corresponds to data collected during a specific and known fault. A discriminant function is applied to diagnose faults using data collected from the plant.
The optimal solution of multi-objective control problem (MOCP) isn't unique, so it is hard for traditional method to obtain these optimal solutions in one simulation process. Based on this background, Multi-Object...
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ISBN:
(纸本)9781605583266
The optimal solution of multi-objective control problem (MOCP) isn't unique, so it is hard for traditional method to obtain these optimal solutions in one simulation process. Based on this background, Multi-Objective Compatible control (MOCC) algorithm was presented by Lihong Xu in [2]. MOCC is a compromise method, which hunts for suboptimal and feasible region as the control aim rather than precise optimal point. The controller of MOCC is optimized by GA in its interval, namely its controller lacks concrete controller structure. Due to the controller without concrete structure, the system model must be accurate and without input disturbance;however, it is impractical in practice. Besides, the control problem is different from the optimization. Different user has different preference and users' preference information plays a key role in control performance. In this paper, the feedback control law u L and users' preference information are incorporated into MOCC algorithm. An improved MOCC (IMOCC) algorithm is presented. The simulation result demonstrates its superiority and advantage over the MOCC algorithm. Copyright 2009 ACM.
This paper proposed a graph-based clustering approach for gene expression data. The new method is based on regulatory network graph obtained from gene expression data. Clustering is performed based on the topological ...
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In this paper, we propose an ordinal optimization theory based algorithm to solve the optimization problem of G/G/1/K polling system with k-limited service discipline for a good enough solution using limited computati...
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Development of the phenomenon of cavitation in cavitation behavior requires knowledge of both plant and equipment working in the facility. This paper presents a diagram of cavitational behavior for a butterfly valve w...
Development of the phenomenon of cavitation in cavitation behavior requires knowledge of both plant and equipment working in the facility. This paper presents a diagram of cavitational behavior for a butterfly valve with a diameter of 100 mm at various openings, which was experimentally built. We proposed seven stages of evolution of the phenomenon of cavitation in the case of a butterfly valve. All these phases are characterized by pressure drop, noise and vibration at various flow rates and flow sections through the valve. The level of noise and vibration for the seven stages of development of the phenomenon of cavitation were measured simultaneously. The experimental measurements were comprised in a knowledge database used in training of a neural network of a neural flow controller that maintains flow rate constantly in the facility by changing the opening butterfly valve. A fuzzy position controller is used to access the valve open. This is the method proposed to provide operational supervision outside the cavitation for a butterfly valve.
In recent years, evolutionary methods have shown great success in solving many combinatorial optimization problems such as FIR (Finite Impulse Response) filter design. An ordinary method in FIR filter design problem i...
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In recent years, many studies show that kernel methods are computationally efficient, robust, and stable for pattern analysis. Many kernel-based classifiers were designed and applied to classify remote-sensed data, an...
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