Nowadays,China are undergoing a thoroughly power market reforming,so there are a lot of research on theory of electricity pricing and trading mechanism *** with that,simulating the power market operation procedure and...
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Nowadays,China are undergoing a thoroughly power market reforming,so there are a lot of research on theory of electricity pricing and trading mechanism *** with that,simulating the power market operation procedure and verify the effect of power market are also an important *** improve the precision of the simulation,one of the key work is to simulate the market participants' behavior *** paper presents a simulation method for GENCOS' based on learning algorithm,which creates bidding decision models for GENCOS' with different decision objectives and reflects the GENCOS' bidding strategy improvement during the market operation.
Urban Parking is a problem that costs time and energy. That is why intelligent parking is a field of research growing very quickly. In a city where no sensor infrastructure within each place is deployed but only a cou...
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Urban Parking is a problem that costs time and energy. That is why intelligent parking is a field of research growing very quickly. In a city where no sensor infrastructure within each place is deployed but only a counting system at every intersection is available, we show that it still possible to propose an efficient method that determines an itinerary that minimizes the expected time to find an available parking place. For this, we first model the urban area by a graph. Then, we implement a learning algorithm that uses a reinforcement learning method. In this model, each agent modeling an intersection, learns the best next street portion. At each step, all the decisions taken by the agents generate an itinerary whose expectation time is the basis for updating the parameters of learning. The execution times and performances of the learning algorithm are compared with those of a method that constructs step by step the itinerary by choosing the next segment with an evaluation of the future expectation time within this segment. We evaluate the performance of the learning algorithm by realistic simulations. The simulation data are extracted from the map of Versailles.
In this paper, we research the real-time object tracking technology. The object tracking algorithm discussed in this paper is developed based on the Tracking-learning-Detection (TLD) and the Centroid Neural Network (C...
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ISBN:
(纸本)9781467380140
In this paper, we research the real-time object tracking technology. The object tracking algorithm discussed in this paper is developed based on the Tracking-learning-Detection (TLD) and the Centroid Neural Network (CNN). The object is unknown ahead of tracking;the model of the object is composed of objects transformed geometrically immediately after tracking. The TLD framework is useful for long-term object tracking in a video stream because the TLD framework applies a novel learning algorithm called P-N learning. We propose a method that applies the CNN algorithm to the TLD framework. The CNN algorithm is an unsupervised learning algorithm that provides a stable result, regardless of initial values of learning coefficients and neurons. The object tracking algorithm discussed in this paper has a higher accuracy than that of TLD in terms of detection. Additionally, it exhibits better processing performance than that of TLD.
Traditional PED neural network adopts BP learning algorithm. However, without accurate gradients, its initial MSE is too large and the procedure of convergence may be unstable. A modified PSO (MPSO) algorithm is intro...
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ISBN:
(纸本)9787810778022
Traditional PED neural network adopts BP learning algorithm. However, without accurate gradients, its initial MSE is too large and the procedure of convergence may be unstable. A modified PSO (MPSO) algorithm is introduced to training the PID neural network. The WSO algorithm does not need any gradient information. It can keep large variety all along and solve premature convergence, which is a major problem in basic PSO algorithm. Simulation results show MPSO algorithm is the best learning algorithm for PID neural network.
Based on immunological antibody clonal selection theory, the general steps of Immune Clonal Selection algorithm (ICSA) are presented in this paper. We put forward the network framework of ICSA, and the dynamic charact...
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Based on immunological antibody clonal selection theory, the general steps of Immune Clonal Selection algorithm (ICSA) are presented in this paper. We put forward the network framework of ICSA, and the dynamic characteristics of ICSA based on the Lyapunov theory are analyzed. Then this paper introduces a novel Artificial Immune System algorithm, Pseudo-Grads Hybrid Immune Clonal Selection Network (GHICSN). The simulation results of some functions optimization indicate that GHICSN improves the performance of ICSA to some extend.
Aimed at the problem of deviation of uncertainty estimates in the test model of attributes selecting with the information gain,an improved learning algorithm of decision tree based on the uncertainty deviation of entr...
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ISBN:
(纸本)9781467321006
Aimed at the problem of deviation of uncertainty estimates in the test model of attributes selecting with the information gain,an improved learning algorithm of decision tree based on the uncertainty deviation of entropy measure was *** the algorithm,the method of regulating oppositely deviation of the information entropy peak through a sine function was used,when test of attributes choice with information gain the adverse effect of deviation of information entropy peak was *** with the ID3,the improvement of classification performance was acquired while its better stability of performance for its decision *** research results show that the rationality of attribute selection test was effectively improved through the method based on the entropy uncertainty deviation.
A two-dimensional iterative learning PID control algorithm with Markov tuning method for batch reaction process is presented in this study. The learning algorithm with parameters tuned by Markov method can be explicit...
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A two-dimensional iterative learning PID control algorithm with Markov tuning method for batch reaction process is presented in this study. The learning algorithm with parameters tuned by Markov method can be explicitly tackling the repetitiveness of batch process, while achieve precise tracking of preset trajectory. It further shows that the control algorithm can guarantee convergence of the tracking error. Simulation results show that the parameters tuned by the Markov method can make the tracking error converge faster compared with the intimal control method.
We analyze a natural learning algorithm for uniform pacing of advertising budgets, equipped to adapt to varying ad sale platform conditions. On the demand side, advertisers face a fundamental technical challenge in au...
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ISBN:
(纸本)9781450394321
We analyze a natural learning algorithm for uniform pacing of advertising budgets, equipped to adapt to varying ad sale platform conditions. On the demand side, advertisers face a fundamental technical challenge in automating bidding in a way that spreads their allotted budget across a given campaign subject to hidden, and potentially dynamic, cost functions. This automation and calculation must be done in runtime, implying a necessarily low computational cost for the high frequency auction rate. Advertisers are additionally expected to exhaust nearly all of their sub-interval (by the hour or minute) budgets to maintain budgeting quotas in the long run. To resolve this challenge, our study analyzes a simple learning algorithm that adapts to the latent cost function of the market and learns the optimalaverage bidding value for a period of auctions in a small fraction of the total campaign time, allowing for smooth budget pacing in real-time. We prove our algorithm is robust to changes in the auction mechanism, and exhibits a fast convergence to a stable average bidding strategy.
Estimating energy consumption rates is a necessary step when building infrastructure for charging and schedule optimization of battery-powered vehicles utilized in public urban driving patterns. This study examined se...
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Estimating energy consumption rates is a necessary step when building infrastructure for charging and schedule optimization of battery-powered vehicles utilized in public urban driving patterns. This study examined several input factors for the prediction of vehicle performance. Input conditions were energy management controls, State of Charge (SOC) power train batteries, and ultra-capacitor vehicle models;output metrics included consumption rates, battery loads, and trip distances. To examine the experimental design, an L9 design was used with four control factors at three different levels each. Artificial neural network (ANN) models were developed employing four learning algorithms: quick propagation (QuP), batch backpropagation (BBaP), Levenberg-Marquardt backpropagation (LMBaP), and incremental backpropagation (IBaP). Post-simulation results were summarized and validated using the root mean square error (RMSE), which indicated that the values collected experimentally were close to those predicted by the models. This paper built an ANN-based prediction model and accurately predicted vehicle performance and potential energy shortfalls in public transportation networks. These insights can be applied to interventions like charging stations or reshaping bus timings to avoid power loss.
Three new learning algorithms for Takagi-Sugeno-Kang fuzzy system based on training error and genetic algorithm are proposed. The first two algorithms are consisted of two phases. In the first phase, the initial struc...
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Three new learning algorithms for Takagi-Sugeno-Kang fuzzy system based on training error and genetic algorithm are proposed. The first two algorithms are consisted of two phases. In the first phase, the initial structure of neuro-fuzzy network is created by estimating the optimum points of training data in input-output space using KNN (for the first algorithm) and Mean-Shift methods (for the second algorithm) and keeps adding new neurons based on an error-based algorithm. Then in the second phase, redundant neurons are recognized and removed using a genetic algorithm. The third algorithm then builds the network in one phase using a modified version of error algorithm used in the first two methods. The KNN method is shown to be invariant to parameter K in KNN algorithm and in two simulated examples outperforms other neuro-fuzzy approaches in both performance and network compactness.
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