搜索资源列表
Competitive_learning
- matlab编写的[数据挖掘]分类算法Competitive_learning算法。-Matlab prepared by the [data mining] classification algorithm Competitive_learn ing algorithm.
LVQ学习矢量化算法
- LVQ学习矢量化算法源程序 This directory contains code implementing the Learning vector quantization network. Source code may be found in LVQ.CPP. Sample training data is found in LVQ1.PAT. Sample test data is found in LVQTEST1.TST and LVQTEST2.TST. The
rbfSrc
- This program demonstrates some function approximation capabilities of a Radial Basis Function Network. The user supplies a set of training points which represent some \"sample\" points for some arbitrary curve. Next, the user specifies the number o
RaoBlackwellisedParticleFilteringforDynamicConditi
- The software implements particle filtering and Rao Blackwellised particle filtering for conditionally Gaussian Models. The RB algorithm can be interpreted as an efficient stochastic mixture of Kalman filters. The software also includes efficient stat
AHybidGeneticAgorithmtoSolveTS
- 求解TSP和MTSP的混合遗传算法_英文_,Abstract:M any app licat ions are invo lved w ith mult ip le salesmen each of w hom visits a subgroup cit ies and returns the same start ing city. The to tal length of all subtours is required to be m ini2 mum. Th is is called
cvhjj
- 该文侧重以A IS 的基本原理框架为线索, 对其 研究状况加以系统综述.-D raw ing in sp irat ion f rom the verteb rate imm une system , a new research f ield of A rt i2 f icial Imm une System (A IS) is sp ringing up.
edrk
- 主要包括免疫识别、免疫学习、免疫 记忆、克隆选择、个体多样性、分布式和自适应等,-It is the real engineering app licat ion s that draw the b road at ten2 t ion of compu ter scien t ist s to recogn ize the great po ten t ial of A IS, hereby som e impo rtan t app li2 cat ion f ields as
TSPandMTSP
- MTSP 问题其实与单 旅行商问题(Traveling Salesperson Problem ,简称TSP) 相似,但是由于添加了任何城市只要被某一旅行商访问到即可这个附加条 件,因而增加了问题复杂度。在以前使用遗传算法(GA) 研究解决MTSP 问题时,通常采用标准的TSP 染色体和处理方法。-M any app licat ions are invo lved w ith mult ip le salesmen each of w hom visits a subgroup c
PS0-SVR
- ：针对发酵过程中生物参数难以实时在线测量的问题，建立了用于生物参数状态预估的 支持向量机软测量模型。考虑到该支持向量回归(SVR)模型的复杂性和冷化特征取决于其三 个参数 ，c， 能否取到最优值，采用粒子群优化(PSO)算法实现对参数 ，c， 的同时寻优。在 此基础上，以饲料用 ．甘露聚糖酶为对象，建立了基于PSO—SVR的发酵过程产物浓度状态预估 模型。发酵罐控制结果表明：该模型具有很好的学习精度和泛化能力，可实现对 ．甘露聚糖酶 产物浓度的实时在线预估。-In
NeuralNetworkFlood
- Flood神经网络程序包，Flood is a comprehensive class library which implements the multilayer perceptron in the C++ programming language. It has been developed follow- ing the functional analysis and calculus of variations theories. In this regard, this
adaptive-genetic-algorithm
- 自适应GA SVM 参数选择算法研究Param eter selection algorithm for support vector machines based on adaptive genetic algorithm 支持向量机是一种非常有前景的学习机器, 它的回归算法已经成功地用于解决非线性函数的逼近问题. 但 是, SVM 参数的选择大多数是凭经验选取, 这种方法依赖于使用者的水平, 这样不仅不能获得最佳的函数逼近效果, 而且采用人工的方法选择 SVM 参数比较浪费
coherence_MVDR_jacob-benesty
- 本文介绍MVDR算法实现及其改进 用于多麦克风语音增强-The minimum variance distortionless response (MVDR), originally developed by Capon for frequency-wavenumber analysis, is a very well established method in array process-ing. It is also used in spectral estimation. The
123
- 提出了一种双域模型人工鱼群算法。算法采用前驱节点指向的编码方法形成多播树表示人工鱼，将搜索 空间分为可行域和非可行域。分别赋予可行域和非可行域的人工鱼不同的游动目标，设计行为算子自适应地执行 4 种人工鱼行为。数值实验结果表明，提出的算法可以有效利用非可行个体，具有较好的求解时延约束最小代价 多播树的性能。-An artificial fish swarm algorithm with two regions model was proposed. The algor ithm us
lunwen
- 新一代高性能无人机飞控系统的研究与设计 张小林 赵宇博 范力思-I n o r de r t o cau se t he U A V f lig ht co nt r o l sy st e m has t he f o r mida ble da t a- ha ndling ca pa cit y , t h e lo w po we r lo ss , t he st r o ng f le x ibilit y an d a hig he r int e g r at io n
UCAV-Path-Planning-Based-on-MAX-MIN
- ：为了保证无人作战飞机(UCAV)以最小的被发现概率和最优的航程到达目标点，在敌方防御区域内执 行任务前必须进行航路规划。蚁群优化(ACO)算法的并行实现机制适合于复杂作战环境下的UCAV航路规划，但是基本ACO算法有易陷于局部最优解的缺点-Abstract：To ensure unmanned combat aerial vehicle(UCAV)to reach the destination with an optimal path and a minimum rate to be
PARTICLE-FILTER-ISSUES
- 针对基于贝叶斯原理的序贯蒙特卡罗粒子滤波器出现退化现象的原因, 以无敏粒子滤波(U PF)、辅助粒子滤波 (A S IR) 及采样重要再采样(S IR) 等改进的粒子滤波算法为例, 对消除该缺陷的关键技术(优化重要密度函数及再采样) 进行了 分析研究。说明通过提高重要密度函数的似然度、引进当前测量值、预增和复制大权值粒子等方式, 可以有效改善算法性能。 最后通过对一无源探测定位问题进行仿真, 验证了运用该关键技术后, 算法的收敛精度和鲁棒性得到进一步增强。- Abstract:W e
MOEA-NSGA-II
- NSGA (No n- Do mina te d So r ting in Ge ne tic Alg o r ithms [5 ]) is a p o pula r no n-do mina tio n ba s e d g e ne tic a lg o r ithm fo r multi- o b je c tive o ptimiz a tio n. I t is a ve r y e ff e c tive a lg o r ithm but ha s b e e n g
Deep_Learning_Neural_NetWork
- deeplearn ing demo cat识别(Neural network demo Cat recognition)