Description: 开发环境:Matlab
简要说明:动量-自适应学习调整算法。在实际应用中，原始的BP算法很难胜任，因此出现了很多的改进算法。BP算法的改进主要有两种途径，一种是采用启发式学习方法，另一种则是采用更有效的优化算法。本例采用动量BP算法，来实现对网络的训练过程，动量法降低了网络对于误差曲面局部细节的敏感性，有效地抑制网络陷于局部极小。-development environment : Matlab Brief Description : Momentum-adaptive learning algorithm adjustments. In practical application, the original BP algorithm competence, resulting in a lot of improved algorithm. BP algorithm improvements There are two main ways of using a heuristic approach to learning Another is the use of a more effective method of optimization. Momentum cases using the BP algorithm to achieve the network training process, Momentum for reducing error of the network for local surface details of the sensitivity, to effectively curb the network into local minima. Platform: |
Size: 1051 |
Author:zhangjian |
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Description: 开发环境:Matlab
简要说明:动量-自适应学习调整算法。在实际应用中，原始的BP算法很难胜任，因此出现了很多的改进算法。BP算法的改进主要有两种途径，一种是采用启发式学习方法，另一种则是采用更有效的优化算法。本例采用动量BP算法，来实现对网络的训练过程，动量法降低了网络对于误差曲面局部细节的敏感性，有效地抑制网络陷于局部极小。-development environment : Matlab Brief Description : Momentum-adaptive learning algorithm adjustments. In practical application, the original BP algorithm competence, resulting in a lot of improved algorithm. BP algorithm improvements There are two main ways of using a heuristic approach to learning Another is the use of a more effective method of optimization. Momentum cases using the BP algorithm to achieve the network training process, Momentum for reducing error of the network for local surface details of the sensitivity, to effectively curb the network into local minima. Platform: |
Size: 1024 |
Author:zhangjian |
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Description: 爬山算法是一种局部择优的方法，采用启发式方法，是对深度优先搜索的一种改进，它利用反馈信息帮助生成解的决策。 属于人工智能算法的一种。-Climbing algorithm is a partial merit-based approach, using heuristic methods, is a depth-first search of an improvement, which uses feedback information to generate solutions to help decision-making. Belong to a kind of artificial intelligence algorithms. Platform: |
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Author:ccy |
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Description: 根据汽车内饰等行业需求,对皮制品加工的优化排样问题进行了研究.创新地采用离散化处理方式,同时引进边界约束,使排样过程与皮料和样片的几何信息无关,使用基于顺序的启发式底左布局将样片顺次布置到皮料上-According to the demand for such industries as automotive interior trim, leather processing for optimal layout problem is studied. Innovative approach of using discrete, while the introduction of boundary constraints, so that the process of scheduling and leather-like material and geometric information of the sample has nothing to do, based on heuristic at the end of the sequence of left-sequential arrangement of the layout of the sample material on the skin Platform: |
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Author:翁經堯 |
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Description: The paper is about bounds for LDPC and LDGM codes under MAP. A new method for analyzing low density parity check (LDPC) codes and low density generator
matrix (LDGM) codes under bit maximum a posteriori probability (MAP) decoding is
introduced. The method is based on a rigorous approach to spin glasses developed by Francesco
Guerra. It allows to construct lower bounds on the entropy of the transmitted message conditional
to the received one. Based on heuristic statistical mechanics calculations, we conjecture
such bounds to be tight. The result holds for standard irregular ensembles when used over
binary input output symmetric channels.
The method is first developed for Tanner graph ensembles with Poisson left degree distribution.
It is then generalized to ‘multi-Poisson’ graphs, and, by a completion procedure, to
arbitrary degree distribution. Platform: |
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Author:mike zhou |
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Description: Simulated Annealing (SA) is a smart (meta)-heuristic for Optimization. Given a cost function in a large search space, SA replaces the current solution by a random "nearby" solution. The nearby solution is chosen with a probability that depends on the difference between the corresponding function values and on a global parameter T (a.k.a the temperature). T is gradually decreased during the process. The current solution changes almost randomly when T is large, but increasingly "downhill" as T goes to zero. The allowance for "uphill" moves saves the method from becoming stuck at local minima.
This approach has some similitude with Physic, where the heat causes the atoms to become unstuck from their initial positions and wander randomly through states of higher energy the slow cooling gives them more chances of finding configurations with lower internal energy than the initial one.-Simulated Annealing (SA) is a smart (meta)-heuristic for Optimization. Given a cost function in a large search space, SA replaces the current solution by a random " nearby" solution. The nearby solution is chosen with a probability that depends on the difference between the corresponding function values and on a global parameter T (aka the temperature). T is gradually decreased during the process. The current solution changes almost randomly when T is large, but increasingly " downhill" as T goes to zero. The allowance for " uphill" moves saves the method from becoming stuck at local minima. This approach has some similitude with Physic, where the heat causes the atoms to become unstuck from their initial positions and wander randomly through states of higher energy the slow cooling gives them more chances of finding configurations with lower internal energy than the initial one. Platform: |
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Author:dingchong |
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Description: Abstract. In this paper, we propose a method of hiding sensitive classification
rules from data mining algorithms for categorical datasets. Our
approach is to reconstruct a dataset according to the classification rules
that have been checked and agreed by the data owner for releasing to
data sharing. Unlike the other heuristic modification approaches, firstly,
our method classifies a given dataset. Subsequently, a set of classification
rules is shown to the data owner to identify the sensitive rules that
should be hidden. After that we build a new decision tree that is constituted
only non-sensitive rules. Finally, a new dataset is reconstructed.
Our experiments show that the sensitive rules can be hidden completely
on the reconstructed datasets. While non-sensitive rules are still able
to discovered without any side effect. Moreover, our method can also
preserve high usability of reconstructed datasets. Platform: |
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Author:Rishi |
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Description: a meta heuristic approach to the dynamic vehicle problem with time windows. Also includes other approaches and comparison of results Platform: |
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Author:Vishal |
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Description: 关于DFT的文章，应用FPGA实现傅立叶变换。-Abstract—We present a domain-specific approach to generate
high-performance hardware-software partitioned implementations
of the discrete Fourier transform (DFT). The partitioning
strategy is a heuristic based on the DFT’s divide-and-conquer
algorithmic structure and fine tuned by the feedback-driven
exploration of candidate designs. We have integrated this approach
in the Spiral linear-transform code-generation framework
to support push-button automatic implementation. We present
evaluations of hardware-software DFT implementations running
on the embedded PowerPC processor and the reconfigurable
fabric of the Xilinx Virtex-II Pro FPGA.
In our experiments, the 1D and 2D DFT’s FPGA-accelerated
libraries exhibit between 2 and 7.5 times higher performance
(operations per second) and up to 2.5 times better energy
efficiency (operations per Joule) than the software-only version. Platform: |
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Author:李然 |
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Description: 地面探测器发现海杂波中的目标一种方法 并有仿真-Based upon observations from real radar data measurements, a
heuristic approach exploiting a salient aspect of the idealized LRT
is developed which is shown to perform well when applied to real
measured sea clutter Platform: |
Size: 2135040 |
Author:yangxiaozhu |
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Description: 蚁群算法(ant colony algorithm，简称ACA)是20世纪90年代由意大利学者M．Dorigo等人首先提出来的一种新型的模拟进化算法．它的出现为解决NP一难问题提供了一条新的途径．用蚁群算法求解旅行商问题(TSP)、分配问题(QAP)、调度问题(JSP)等，取得了一系列较好的实验结果．虽然对蚁群算法研究的时间不长，但是初步研究已显示出蚁群算法在求解复杂优化问题(特别是离散优化问题)方面具有一定的优势，表明它是一种很有发展前景的方法．蚁群算法的主要特点是：正反馈、分布式计算．正反馈过程使它能较快地发现问题的较好解；分布式易于并行实现，将它与启发式算法相结合，易于发现较好解．-ACO (ant colony algorithm, referred to as ACA) is the 1990s by the Italian scholar M. Dorigo, who first proposed a new type of simulated evolutionary algorithm. It appears to solve NP-hard problem provides a new way. Ant colony algorithm for traveling salesman problem (TSP), distribution (QAP), scheduling problems (JSP), etc., made a series of good results. Although the ant colony algorithm is not long, but preliminary studies have shown that the ant colony algorithm in solving complex optimization problems (in particular, discrete optimization problem) has certain advantages, that it is a promising approach. The main features of ant colony algorithm is: positive feedback, distributed computing. Positive feedback process so that it can quickly find a better solution of the problem distributed easy-to-parallel implementation, it would be combined with the heuristic algorithm, easy to find better solutions. Platform: |
Size: 2048 |
Author:咋都有 |
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Description: clips最新版
CLIPS是一种专家系统工具，最初由NASA/Lyndon B. Johnson太空中心软件技术研究室开发出来。自1986年首次发布以来，CLIPS经历了不断的改进和完善。现在它已经被广泛的应用在数以万计的全球用户中。 CLIPS被开发出来以促进集成人类知识和经验的软件发展。 在CLIPS中，知识的表示有三种方式： l 规则，规则表示法是基于启发式经验知识库的首要选择。 l 自定义函数和通用函数，这种方式是程序式知识表示的首选。 l 面向对象设计，也是程序式知识表示的首选。面向对象的程序设计被支持的5个普遍接受的特征是：类，消息处理函数，抽象，封装，继承和多态性。模式匹配可以是对象和事实。 你可以仅用规则，或者仅用对象或者两者混合使用来开发软件。 CLIPS同时支持与其他语言的集成，如C和Java。事实上，CLIPS是C Language Integrated Production的缩写。规则能基于事实与对象的匹配，规则和对象同时组成了一个集成系统。除了被当作一个独立的工具之外，CLIPS还能被程序语言调用，运行其函数，然后返回给调用函数控制权。同样的，程序代码也能作为一个外部函数在CLIPS中被定义和调用。当外部代码执行完毕后，控制权返回到CLIPS
-CLIPS is an expert system tool, originally developed by the Software Technology Laboratory of the NASA/Lyndon B. Johnson Space Center. Since its first release in 1986, CLIPS has undergone continuous improvement and perfection. It is now widely used in tens of thousands of users worldwide. CLIPS was developed to facilitate the integration of human knowledge and experience in software development. In CLIPS, knowledge representation in three ways: l rules, rules, notation is the first choice of heuristic experience-based knowledge base. l-defined functions and general functions, this approach is the first choice of the procedural knowledge. l object-oriented design, is also the program of choice for knowledge representation. The five generally accepted features of object-oriented programming support: classes, message-handling functions, abstraction, encapsulation, inheritance and polymorphism. Pattern matching can be the objects and facts. You can only rule, or only to develop the softwar Platform: |
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Author:魏林 |
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Description: IEEE文章，启发式搜索策略用于
确定最佳的电容布局和
供配电系统的评等。在启发式
方法提出了一个关键的少数
名为“敏感节点，节点，选择
安装电容器优化净储蓄
同时实现了大的整体亏损减少。
这种方法保证了电压约束
会见。这种启发式的方法是适合大
配电系统，并可以在网上有用
实施。该方法是
用于测试系统和结果
与其他已发表的技术。
-Heuristic search strategies are used to
determine the optimum capacitor placement and
ratings for distribution systems. In the heuristic
approach proposed a small number of critical
nodes, named sensitive nodes, are selected for
installing capacitors that optimise the net savings
while achieving a large overall loss reduction.
This method insures that voltage constraints are
met. This heuristic approach is suitable for large
distribution systems and can be useful in online
implementation. The proposed approach is
applied to a test system and the results are
compared with other published techniques. Platform: |
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Author:徐正 |
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Description: KMEAN C#
In data mining, k-means clustering is a method of cluster analysis which aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean. This results in a partitioning of the data space into Voronoi cells.
The problem is computationally difficult (NP-hard), however there are efficient heuristic algorithms that are commonly employed and converge fast to a local optimum. These are usually similar to the expectation-maximization algorithm for mixtures of Gaussian distributions via an iterative refinement approach employed by both algorithms. Additionally, they both use cluster centers to model the data, however k-means clustering tends to find clusters of comparable spatial extent, while the expectation-maximization mechanism allows clusters to have different shapes. Platform: |
Size: 2048 |
Author:Truong |
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Description: he increasing demand of networks, over the past decade has
led to the need for enormous cost reduction and increasing
accessibility of networks. It requires for efficient traffic management in the network and ability to expand as per the need. Various network design problem solutions are based on enumerative and heuristic approach have been defined in the literature. ,he increasing demand of networks, over the past decade has
led to the need for enormous cost reduction and increasing
accessibility of networks. It requires for efficient traffic management in the network and ability to expand as per the need. Various network design problem solutions are based on enumerative and heuristic approach have been defined in the literature. Platform: |
Size: 2951168 |
Author:Mohd Elsoufi |
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Description: Differential Evolution (DE) algorithm is a new heuristic approach mainly having three advantages
finding the true global minimum regardless of the initial parameter values, fast convergence, and using
few control parameters Platform: |
Size: 133120 |
Author:f&f |
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Description: Spoil names of symbols with C linkage, so use an heuristic approach to check if the name should be demangled.
-Spoil names of symbols with C linkage, so use an heuristic approach to check if the name should be demangled.
Platform: |
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Author:tiegiudk |
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Search - Heuristic Approach