In recent years,along with the further study in incomplete information chess—military *** to express and address the incomplete information during the process of game,deciding certain moves strategies to obtain highe...
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ISBN:
(纸本)9781479970186
In recent years,along with the further study in incomplete information chess—military *** to express and address the incomplete information during the process of game,deciding certain moves strategies to obtain higher winning rate increasingly become a new *** approach using digit to express incomplete information in military chess is proposed in this paper,a model based on guessing probability to address incomplete information in military chess game is designed,and the programming process is *** the National Computer Games Tournament,the game program using this model is stronger than the *** are shown as 6 wins,1 draw,and 1 *** average time of each moving step is shorter than opponent 2 second.
In the era of big data and advanced computational capabilities,financial market participants are continuously searching for innovative strategies to gain a competitive edge.A potential pathway emerges in the domain of...
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In the era of big data and advanced computational capabilities,financial market participants are continuously searching for innovative strategies to gain a competitive edge.A potential pathway emerges in the domain of deep learning,especially with regard to the LSTM neural architectures,which are renowned for their ability to handle and make predictions based on time series *** study delves into the utilization of LSTM for predicting stock prices,emphasizing the advantages of dynamic investment portfolios in the rapidly fluctuating market *** a dynamic window approach for time series data preprocessing,a self-attention mechanism-LSTM model was designed to anticipate the tendency of annual closing prices for five stocks from 2022 to 2023,Utilizing the initial 80%of stock price data as training set and allocating the residual 20% for *** performance of the dynamic optimization portfolio model was assessed by dynamically adjusting the weights of the stocks based on the last 20% of the data,and was subsequently compared to actual market cumulative *** findings indicate not only that the LSTM model offers a commendable level of accuracy in predicting stock prices,but also that the recursive algorithm for the dynamic optimization portfolio,constrained by maximum returns and minimal standard deviation,consistently outperforms the general market.
A new approach for measuring the shape and surface of an object observed from a single view is proposed. The proposed approach is based on using a single hidden layer wavelet neural network (WNN) t
A new approach for measuring the shape and surface of an object observed from a single view is proposed. The proposed approach is based on using a single hidden layer wavelet neural network (WNN) t
This paper derives the recursive formulas of the computation of the criterion functions for the well-known weighted recursive least squares algorithm and the finite-data-window recursive least squares algorithm for li...
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This paper derives the recursive formulas of the computation of the criterion functions for the well-known weighted recursive least squares algorithm and the finite-data-window recursive least squares algorithm for linear regressive models. The analysis indicates that the proposed recursive computation formulas can be extended to the least squares estimation algorithms for pseudo-linear regression models, e.g., the equation error systems.
Lie groups and Lie algebras are used to research the recursive dynamics of flexible multi-body systems with the lumped-parameter method. First the adjoint transformations and adjoint operators of Lie groups and Lie al...
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Lie groups and Lie algebras are used to research the recursive dynamics of flexible multi-body systems with the lumped-parameter method. First the adjoint transformations and adjoint operators of Lie groups and Lie algebras are discussed. At the same time, the dynamical modeling method with active and passive joints is built. Then the flexible body is discretized into a collection of rigid bodies. These new bodies and the original bodies are corresponding to active and passive joints. Finally a four-bar model with flexible body is simulated with above method. The simulation results show that with the method can be solved quickly and efficiently. Also geometric nonlinear deformation is considered.
This paper derives the recursive formulas of the computation of the criterion functions for the well-known weighted recursive least squares algorithm and the finite-data-window recursive least squares algorithm for li...
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This paper derives the recursive formulas of the computation of the criterion functions for the well-known weighted recursive least squares algorithm and the finite-data-window recursive least squares algorithm for linear regressive models. The analysis indicates that the proposed recursive computation formulas can be extended to the least squares estimation algorithms for pseudo-linear regression models, e.g., the equation error systems.
Bayesian classification is a hotspot of machine learning,the purpose of this paper is to train an efficient Bayesian classification based on *** expounded the Bayes' theorem and the concept of Bayes classifier as ...
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Bayesian classification is a hotspot of machine learning,the purpose of this paper is to train an efficient Bayesian classification based on *** expounded the Bayes' theorem and the concept of Bayes classifier as the foundation,and then,used the recursive algorithm to create a Bayesian data set;the SQL query codes to count classification properties,decision attribute values and classcondition in the training sample,and to calculate the corresponding probability;and the SQL query-update codes to mark the most optimal decision value,thus,the high accuracy of Bayesian classifier model had been ***,we applied the above classification system to predict and determinate the students' *** is preliminarily shown that Bayesian classification method has better application prospect in predicting students' grades.
Discrete short time Fourier transformation （DSTFT） is a common and effective method in digital signal processing and analysis. In the most applications, amplitude information of Fourier transform is utilized instead...
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Discrete short time Fourier transformation （DSTFT） is a common and effective method in digital signal processing and analysis. In the most applications, amplitude information of Fourier transform is utilized instead of phase information. An algorithm based on the sliding DSTFT is presented in this paper. The demodulation of DQPSK signal is achieved using phase information in frequency domain. The principle and process of the algorithm are detail researched. The fast recursive algorithm is given under the two window functions, and the bit error rate （BER） is analyzed. The simulation shows that the DSTFT-based algorithm can get high performance, and facilitate software real-time processing. In the Gaussian white noise channel, when BER is 10-4, the performance of the algorithm is nearly 1dB better than the traditional differential detection method.
＜正＞Following the definition of a two bivariate matrix Pade-type（BMPTA） in[13],the least-squares solution to BMPTA is given and its determinantal expressions are *** avoid the computation of determinants,a recursi...
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＜正＞Following the definition of a two bivariate matrix Pade-type（BMPTA） in[13],the least-squares solution to BMPTA is given and its determinantal expressions are *** avoid the computation of determinants,a recursive algorithm called Sylvester-type algorithm is *** the end the method is applied to partial realization problems of 2-D linear systems.
Most subspace identification methods are developed for linear time-invariant system. However, in reality, most systems are time-varying. Hence the recursive version of subspace identification methods is urgently desir...
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ISBN:
(纸本)9781424472352
Most subspace identification methods are developed for linear time-invariant system. However, in reality, most systems are time-varying. Hence the recursive version of subspace identification methods is urgently desired. In this paper, we propose a unifying framework of recursive subspace model identification algorithm, which is based on the orthogonal projection and principal component analysis (PCA). Based on our framework, the bona fide recursive algorithm is applied to update the QR factorization. Two recursive subspace model identification algorithms are developed for open loop and closed loop condition, respectively. The numerical simulations demonstrate the efficiency of the two algorithms comparing with other algorithms.
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