44715797
Constrained Nonlinear State Estimation - A Differential Evolution Based Moving Horizon Approach
A solution is proposed to estimate the states in the nonlinear discrete time system. Moving Horizon Estimation (MHE) is used to obtain the approximated states by minimizing a criterion that is the Euclidean form of the difference between the estimated outputs and the measured ones over a finite time horizon. The differential evolution (DE) algorithm is incorporated into the implementation of MHE in order to solve the optimization problem which is presented as a nonlinear programming problem due to the constraints. The effectiveness of the approach is illustrated in simulated systems that have appeared in the moving horizon estimation literature.
39138289
Designing Neural Networks Using PSO-Based Memetic Algorithm
This paper proposes an effective particle swarm optimization (PSO) based memetic algorithm (MA) for designing artificial neural network. In the proposed PSO-based MA (PSOMA), not only the evolutionary searching mechanism of PSO characterized by individual improvement plus population cooperation and competition is applied to perform the global search, but also several adaptive high-performance faster training algorithms are employed to enhance the local search, so that the exploration and exploitation abilities of PSOMA can be well balanced. Moreover, an effective adaptive Meta-Lamarckian learning strategy is employed to decide which local search method to be used so as to prevent the premature convergence and concentrate computing effort on promising neighbor solutions. Simulation results and comparisons demonstrate the effectiveness and efficiency of the proposed PSOMA.
21860578
An Effective PSO-Based Memetic Algorithm for Flow Shop Scheduling
This paper proposes an effective particle swarm optimization (PSO)-based memetic algorithm (MA) for the permutation flow shop scheduling problem (PFSSP) with the objective to minimize the maximum completion time, which is a typical non-deterministic polynomial-time (NP) hard combinatorial optimization problem. In the proposed PSO-based MA (PSOMA), both PSO-based searching operators and some special local searching operators are designed to balance the exploration and exploitation abilities. In particular, the PSOMA applies the evolutionary searching mechanism of PSO, which is characterized by individual improvement, population cooperation, and competition to effectively perform exploration. On the other hand, the PSOMA utilizes several adaptive local searches to perform exploitation. First, to make PSO suitable for solving PFSSP, a ranked-order value rule based on random key representation is presented to convert the continuous position values of particles to job permutations. Second, to generate an initial swarm with certain quality and diversity, the famous Nawaz-Enscore-Ham (NEH) heuristic is incorporated into the initialization of population. Third, to balance the exploration and exploitation abilities, after the standard PSO-based searching operation, a new local search technique named NEH_1 insertion is probabilistically applied to some good particles selected by using a roulette wheel mechanism with a specified probability. Fourth, to enrich the searching behaviors and to avoid premature convergence, a simulated annealing (SA)-based local search with multiple different neighborhoods is designed and incorporated into the PSOMA. Meanwhile, an effective adaptive meta-Lamarckian learning strategy is employed to decide which neighborhood to be used in SA-based local search. Finally, to further enhance the exploitation ability, a pairwise-based local search is applied after the SA-based search. Simulation results based on benchmarks demonstrate the effectiveness of the PSOMA. Additionally, the effects of some parameters on optimization performances are also discussed
39231279
An Intelligent Differential Evolution Algorithm for Designing Trading-Ratio System of Water Market
As a novel optimization technique, neural network based optimization has gained much attention and some applications during the past decade. To enhance the performance of Differential Evolution Algorithm (DEA), which is an evolutionary computation technique through individual improvement plus population cooperation and competition, an intelligent Differential Evolution Algorithm (IDEA) is proposed by incorporating neural network based search behaviors into classic DEA. Firstly, DEA operators are used for exploration by updating individuals so as to maintain the diversity of population and speedup the search process. Secondly, a multi-layer feed-forward neural network is employed for local exploitation to avoid being trapped in local optima and improve the convergence of the IDEA. Simulation results and comparisons based on well-known benchmarks and optimal designing of trading-ratio system for water market demonstrate that the IDEA can effectively enhance the searching efficiency and greatly improve the searching quality.
1542038
Analog circuit optimization system based on hybrid evolutionary algorithms
This paper investigates a hybrid evolutionary-based design system for automated sizing of analog integrated circuits (ICs). A new algorithm, called competitive co-evolutionary differential evolution (CODE), is proposed to design analog ICs with practical user-defined specifications. On the basis of the combination of HSPICE and MATLAB, the system links circuit performances, evaluated through electrical simulation, to the optimization system in the MATLAB environment, once a circuit topology is selected. The system has been tested by typical and hard-to-design cases, such as complex analog blocks with stringent design requirements. The results show that the design specifications are closely met, even in highly-constrained situations. Comparisons with available methods like genetic algorithms and differential evolution, which use static penalty functions to handle design constraints, have also been carried out, showing that the proposed algorithm offers important advantages in terms of optimization quality and robustness. Moreover, the algorithm is shown to be efficient.
28818922
An Effective PSO-Based Hybrid Algorithm for Multiobjective Permutation Flow Shop Scheduling
This paper proposes a hybrid algorithm based on particle swarm optimization (PSO) for a multiobjective permutation flow shop scheduling problem, which is a typical NP-hard combinatorial optimization problem with strong engineering backgrounds. Not only does the proposed multiobjective algorithm (named MOPSO) apply the parallel evolution mechanism of PSO characterized by individual improvement, population cooperation, and competition to effectively perform exploration but it also utilizes several adaptive local search methods to perform exploitation. First, to make PSO suitable for solving scheduling problems, a ranked-order value (ROV) rule based on a random key technique to convert the continuous position values of particles to job permutations is presented. Second, a multiobjective local search based on the Nawaz-Enscore-Ham heuristic is applied to good solutions with a specified probability to enhance the exploitation ability. Third, to enrich the searching behavior and to avoid premature convergence, a multiobjective local search based on simulated annealing with multiple different neighborhoods is designed, and an adaptive meta-Lamarckian learning strategy is employed to decide which neighborhood will be used. Due to the fusion of multiple different searching operations, good solutions approximating the real Pareto front can be obtained. In addition, MOPSO adopts a random weighted linear sum function to aggregate multiple objectives to a single one for solution evaluation and for guiding the evolution process in the multiobjective sense. Due to the randomness of weights, searching direction can be enriched, and solutions with good diversity can be obtained. Simulation results and comparisons based on a variety of instances demonstrate the effectiveness, efficiency, and robustness of the proposed hybrid algorithm.
17698779
Neural Network Identification Method Applied to the Nonlinear System
A kind of neural network solution method has been proposed in the paper aiming at a class of non-linear process control system with the characteristic of time delay. In this scheme, a new-type associative memory neural network is used to model the controlled system, and the fuzzy neural network with inverse identification structure is adopted to control the nonlinear process system. This fuzzy neural network control method adopts the structure of three layers combine neural network identifier with inverse structure. Computer simulation and lab application show that it is effective to adopt this scheme to control on-linear process system with time delay.
26759633
Adaptive video coding using mixed-domain filter banks having optimal-shaped subbands
This contribution presents a mixed-domain filter bank. This filter bank can realize arbitrary (and thereby optimal) spectral decomposition and therefore is very suitable for the efficient subband coding of video signals. A coding scheme using the proposed filter bank is considered, which takes advantage of the spectral distribution of the signal and is especially selective to motion.<<ETX>>
36269081
Semi-Data-Aided Nonlinear Carrier Recovery for BPSK Burst-Mode Transmissions
This paper focuses on carrier frequency recovery for BPSK burst transmissions over AWGN channel. Semi-data- aided (SDA) nonlinear frequency estimation algorithms based on maximum-likelihood (ML) principle are derived. According to the algorithms, fewer pilot symbols and some unknown data symbols can be used together for frequency estimation in order that both estimation accuracy and spectral efficiency can be satisfied simultaneously. Simulations are employed to assess the performance of the algorithms. The corresponding Cramer-Rao bounds are discussed. The theoretical analysis of the threshold effect of one estimator is also presented and compared with simulation results.
11669811
Multi-level organization and summarization of the discovered rules
Many existing data mining techniques often p roduce a large number of rules, which make it very difficult for manual inspection o f the rules to identify those interesting ones. This problem represents a major gap between the results of data mining and the understanding and use of the mining results. In this paper, we a rgue that t he key problem is not with the large number of rules because if there are indeed many rules that exist in data, they should b e discovered. The main p roblem is with ou r inability to organize, summarize and present the rules in such a way that they can b e ea sily analyzed b y the user. In this paper, we propose a technique to intuitively organize a nd summarize the discovered rules. With this organization, the discovered rules can b e presented to the user in the way as we think and talk about knowledge in ou r daily lives. This organization also allows the user to view the discovered rules at different levels of details, and to focus his/her attention on those interesting aspects. This paper presents this technique a nd u ses it t o o rganize, summarize a nd present t he knowledge e mbedded in a decision tree, and a set of association rules. Experiment results and p ractical applications show that the technique is both intuitive and effective.
11706749
A Graph-Based Algorithm for Mining Maximal Frequent Itemsets
Association rule mining is an important research branch of data mining, and computing frequent itemsets is the main problem. The paper is designed to find maximal frequent itemsets only. It presents an algorithm based on a frequent pattern graph, which can find maximal frequent itemsets quickly. A breadth-first-search and a depth-first-search techniques are used to produce all maximal frequent itemsets of a database. The paper also analyzes the complexity of the algorithm, and explains the computation procedure by examples. It has high time efficiency and less space complexity for computing maximal frequent itemsets.