Search - co-adaptive

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[AI-NN-PR] Adaptive_Filter_Matlab_code DL : 0: Date : 2025-12-30 Size : 97kb User : 刘英超
[AI-NN-PR] java_evolutionary_algorithms DL : 0: 用Java实现的进化算法包。包括遗传算法、粒子群算法、memetic算法和进化策略算法。-evolutionary-algorithm Evolutionary Algorithm package implemented using Java. The package serves as a foundation class library, supporting the implementation many variants of Evolutionary Algorithms, currently including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Memetic Algorithm (MA), Evolution Strategy (ES). Highlighted features · Support both binary & real-coded string representations of solution · Operator-based design for flexibility · EA Operators: Selection, Crossover, Mutation, Move operators in PSO & and the adaptive scheme in EA · Individual learning: Davidon–Fletcher–Powell (DFP) and Davies, Swann, and Campey with Gram-Schmidt orthogonalization (DSCG) strategies and Random Mutation Hill-climbing (RMHC) In addition, algorithm pipeline which is specified by XML file is also provided for practitioner to configure & design evolutionary algorithms at ease. User can edit runtime & algorithm parameters in the configuration file (XML) & issue the co
Date : 2025-12-30 Size : 102kb User : 陈雷
[AI-NN-PR] Adaptive-Hysteresis DL : 0: 基于径向基函数神经网络迟滞非线性自适应控制提出了一种新的动态迟滞非线性模型. 将一定数量不同死区宽度的 backlash 模型并行相加, 作为一个动态系统以仿真执行器中的迟滞特性. 利用该模型, 采用伪控制方法设计了一套具有未知迟滞特性非线性系统的神经网络自适应控制方案, 通过自适应算法来调整干扰项的上限. 采用 Lyapunov 稳定性理论进行了严格证明, 仿真试验验证了所提方案的有效性.- A nov el class of hysteresis mo dels w as proposed. A cer tain num ber o f different deadband w idth backlash models are superposed, w hich represents a dynamics to m im ic hysteresis in the actuator. With the mo del proposed, an radial basis function neural netw ork ( RBFN )-based adaptive control scheme for nonlinear sy stems w ith unknow n hysteresis nonlinearity w as dev elo ped. The control scheme adopts the de- sign method of pseudo-co ntro l. Witho ut the assumption of boundedness of disturbance term , it is tuned thr oug h adaptive algo rithm . The stability is rigidly pr oved v ia Lyapunov theory and the effectiveness of the pro posed contr ol scheme is illustrated through simulatio n.
Date : 2025-12-30 Size : 207kb User :
[AI-NN-PR] Adaptive-Embedding-Dimension DL : 0: 嵌入维数自适应最小二乘支持向量机状态时间序列预测方法 Condition Time Series Prediction Using Least Squares Support Vector Machine with Adaptive Embedding Dimension 针对航空发动机状态时间序列预测中嵌入维数难于有效选取的问题, 提出一种基于嵌入维数自适应最小二乘支持向量机( L SSVM ) 的预测方法。该方法将嵌入维数作为影响状态时间序列预测精度的重要参数, 以交叉验证误差为评价准则, 利用粒子群优化( P SO ) 进化搜索LSSV M 预测模型的最优超参数与嵌入维数, 同时通过矩阵变换原理提高交叉验证过程的计算效率, 并最终建立优化后的L SSVM 预测模型。航空发动机排气温度( EGT ) 预测实例表明, 该方法可自适应选取适用于状态时间序列预测的最优嵌入维数且预测精度高, 适用于航空发动机状态时间序列预测。- T o deal wit h the difficulty of selecting an appro pr iate embedding dimension for aeroeng ine co ndition time series predictio n, a metho d based o n least squar es suppo rt vecto r machine ( L SSVM ) with ada ptive em bedding dimension is pro po sed. I n the method, the embedding dimensio n is identified as a parameter that af fects the accuracy o f the aer oengine condition time series predictio n par ticle sw arm o ptimizat ion ( P SO) is ap plied to optimize the hyperpar ameter s and embedding dimension of the L SSV M pr edict ion model cro ssv alida tion is applied to evaluate the perfo rmance o f the L SSVM predictio n mo del and matr ix tr ansfo rm is applied to the L SSVM pr ediction model tr aining to accelerate the crossvalidation evaluation pro cess. Ex periments on an aeroengine ex haust g as t emperatur e ( EGT ) predictio n demonst rates that the metho d is hig hly effective in em bedding dimension selection. In compar ison w ith co nv
Date : 2025-12-30 Size : 334kb User :
[AI-NN-PR] lms_f DL : 0: LMS算法是一种重要的自适应滤波算法，此文是对LMS算法进行了修正，并且应用于同址干扰滤波的问题。-LMS algorithm is a kind of important adaptive filtering algorithm, this article is on the LMS algorithm is modified, and applied to co-site interference filtering problem.
Date : 2025-12-30 Size : 364kb User : wentao