A novel methodology is proposed for the development of neural network models for complex engineering systems exhibiting nonlinearity. This method performs neural network modeling by first establishing some fundamental...
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A novel methodology is proposed for the development of neural network models for complex engineering systems exhibiting nonlinearity. This method performs neural network modeling by first establishing some fundamental nonlinear functions from a priori engineering knowledge, which are then constructed and coded into appropriate chromosome representations. Given a suitable fitness function, using evolutionary approaches such as genetic algorithms, a population of chromosomes evolves for a certain number of generations to finally produce a neural network model best fitting the system data. The objective is to improve the transparency of the neural networks, i.e. to produce physically meaningful "white box" neural network model with better generalization performance. In this paper, the problem formulation, the neural network configuration, and the associated optimizationsoftware are discussed in detail. This methodology is then applied to a practical real-world system to illustrate its effectiveness.
Video streaming imposes high rate requirement and stringent constraints on resource limited mesh networks. In this work, we develop a distributed asynchronous particle swarm optimization (DAPSO) algorithm for resource...
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In this paper we present SOC-NLNA, a systematic synthesis methodology for fully integrated narrow-band CMOS Low Noise Amplifiers (LNA) in highperformance System-on-Chip (SoC) designs. SOC-NLNA is based on determinist...
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ISBN:
(纸本)1595933816
In this paper we present SOC-NLNA, a systematic synthesis methodology for fully integrated narrow-band CMOS Low Noise Amplifiers (LNA) in highperformance System-on-Chip (SoC) designs. SOC-NLNA is based on deterministic numerical nonlinearoptimization and the Normal Boundary Intersection (NBI) method for Pareto optimization. To enable SoC integration, we simultaneously optimize both devices and passive components to yield integrated inductor values that are significantly less than those generated by traditional design techniques. When the synthesized LNAs are simulated using Cadence SpectreRF, SOC-NLNA yields up to 35 and 58 percent improvement in noise figure and gain. Leveraging the efficiency of our methodology, we are able to generate the Pareto surfaces between LNA performance metrics in seconds.
Quantum-behaved Particle Swarm optimization (QPSO) is a novel optimization algorithm proposed in the previous work. Compared to the original Particle Swarm optimization (PSO), QPSO is global convergent, while the PSO ...
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ISBN:
(纸本)3540370250
Quantum-behaved Particle Swarm optimization (QPSO) is a novel optimization algorithm proposed in the previous work. Compared to the original Particle Swarm optimization (PSO), QPSO is global convergent, while the PSO is not. This paper focuses on exploring the applicability of the QPSO to data clustering. Firstly, we introduce the K-means clustering algorithm and the concepts of PSO and QPSO. Then we present how to use the QPSO to cluster data vectors. After that, experiments are implemented to compare the performance of various clustering algorithms. The results show that the QPSO can generate good results in clustering data vectors with tolerable time consumption.
A fully automated application was developed and used for the registration of T1-weighted magnetic resonance images (MRIs) for Alzheimer patients. Two methods for image registration were implemented and compared: affin...
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ISBN:
(纸本)0819463892
A fully automated application was developed and used for the registration of T1-weighted magnetic resonance images (MRIs) for Alzheimer patients. Two methods for image registration were implemented and compared: affine and nonlinear registration. nonlinear registration uses continuum-mechanics-based elastic deformation. The affine registration algorithm is linear and is generated by an amplitude-modulated phase-only filter. The nonlinear registration method uses an elastic transformation generated by Navier-Stokes continuum-mechanics models. The validation method to quantitatively compare the performance of the affine and nonlinear registration algorithms uses root-mean-square error and three-dimensional volume rendering.
Area efficiency is one of the major considerations in constraint aware hardware/software partitioning process. This paper models hardware/software partitioning as an optimization problem with the objective of minimizi...
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Evolutionary computing (EC) comprises a family of global optimization techniques that start with a random population of potential solutions and then evolve more fit solutions over many generations. To accomplish this ...
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ISBN:
(纸本)9780769527970
Evolutionary computing (EC) comprises a family of global optimization techniques that start with a random population of potential solutions and then evolve more fit solutions over many generations. To accomplish this increase in fitness, EC uses basic operations like selection, recombination, and mutation. Because of its compute- intensive nature, EC research is an obvious candidate for hosting on HPC clusters or systems. EC requires highperformance computers (HPC) because the selection process needs to evaluate the fitness of each member of a population of solutions, so the more fit individuals may propagate their characteristics to the next generation of solutions. This requirement becomes even more acute because the evaluation process must be iterated over a very large number of generations. In this paper, we provide a general overview of EC, its applicability to a broad range of problems. In particular, we focus on some subclasses of EC known as genetic programming (GP), genetic algorithms (GA), hybrids, and other EC forms. This paper also discusses the architectural issues of hosting EC on a HPC cluster, and the related issue of population management. Two possible EC architectures are presented: (1) a single chromosome evaluator that treats a pool of cluster nodes as evaluators for an individual solution, and (2) a parallel evolver that manages a sub-population of solutions at each node. Advantages and disadvantages of each approach will be discussed. EC may be applied to a wide variety of problems. Applications of EC include schedule optimization, robotic navigation, image enhancement/processing, discrimination of buried unexploded ordnance, discovery of innovative electronic filter and controller designs, lens design optimization, radar response modeling, and many more. EC excels at solving high-dimensional and nonlinear-problems. HPC resources have enabled the broader application of EC optimization techniques. However, at present, EC is underutilized in the
Pattern recognition is a resource intensive task which includes feature extraction, feature selection and classification. Optimizing any of these steps can significantly improve performance. Evolutionary computation m...
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ISBN:
(纸本)9781932415988
Pattern recognition is a resource intensive task which includes feature extraction, feature selection and classification. Optimizing any of these steps can significantly improve performance. Evolutionary computation methods are utilized to address optimization problems that explore a huge, nonlinear and multidimensional search space. In this paper a new distributed framework is introduced which greatly reduces the computation time of such systems, concentrating on feature extraction and feature selection which will operate parallel. This software architecture incorporates Mother Nature's most powerful tools, "evolution" and "parallelism", in its design, while maintaining robustness.
QoS multicast routing in networks is a very important research issues in the areas of networks and distributed systems. Because of its NP-completeness, many heuristics such as Genetic algorithms (GAs) are employ solve...
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ISBN:
(纸本)3540473319
QoS multicast routing in networks is a very important research issues in the areas of networks and distributed systems. Because of its NP-completeness, many heuristics such as Genetic algorithms (GAs) are employ solve the QoS routing problem. Base on the previously proposed Quantum-behaved Particle Swarm optimization (QPSO), this paper proposes a QPSO-based QoS multicast routing algorithm. The proposed method converts the QoS multicast routing problem into an integer programming problem and then solve the problem by QPSO. We test QPSO-base routing algorithm on a network model. For performance comparison, we also test Particle Swarm optimization (PSO) algorithm and GA. The experiment results show the availability and efficiency of QPSO on the problem and its superiority to PSO and GA.
Recently several manifold learning algorithms have been presented for nonlinear dimensionality reduction. Isomap is one of them. However, Isomap suffers from a deficiency that it does not give an explicit mapping func...
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ISBN:
(纸本)1424400600
Recently several manifold learning algorithms have been presented for nonlinear dimensionality reduction. Isomap is one of them. However, Isomap suffers from a deficiency that it does not give an explicit mapping function, which is from high dimensional space to low dimensional target space. In this paper, a version of Isomap with explicit mapping, called E-Isomap, is proposed. In E-Isomap, the geodesic distance matrix is fed into a cost function and then Iterative Majorization is adopted to solve an optimization problem for obtaining both the low dimensional configuration and the nonlinear mapping. Owing to the existence of explicit mapping, this version of Isomap can be more easily used in pattern recognition than the original ones. The experiments on two benchmark data sets are given to demonstrate the performance of the presented method.
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