標(biāo)題: Titlebook: Dealing with Complexity; A Neural Networks Ap Mirek Kárny,Kevin Warwick,Vera K?rková Book 1998 Springer-Verlag London Limited 1998 artifici [打印本頁] 作者: Flexible 時(shí)間: 2025-3-21 19:24
書目名稱Dealing with Complexity影響因子(影響力)
書目名稱Dealing with Complexity影響因子(影響力)學(xué)科排名
書目名稱Dealing with Complexity網(wǎng)絡(luò)公開度
書目名稱Dealing with Complexity網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Dealing with Complexity被引頻次
書目名稱Dealing with Complexity被引頻次學(xué)科排名
書目名稱Dealing with Complexity年度引用
書目名稱Dealing with Complexity年度引用學(xué)科排名
書目名稱Dealing with Complexity讀者反饋
書目名稱Dealing with Complexity讀者反饋學(xué)科排名
作者: Pericarditis 時(shí)間: 2025-3-21 21:19
Statistical Decision Making and Neural Networks,ision making is understood in a wide sense that covers pattern recognition, cluster analysis, parameter estimation, prediction, diagnostics, fault detection, control design etc. In any of these tasks, the available information is processed in order to make some action: to assign a proper class to an作者: sclera 時(shí)間: 2025-3-22 01:33
A Tutorial on the EM Algorithm and Its Applications to Neural Network Learning,can be viewed as universal approximators of non-linear functions that can learn from examples. This chapter focuses on an iterative algorithm for training neural networks inspired by the strong correspondences existing between NNs and some statistical methods [1][2]. This algorithm is often consider作者: hermetic 時(shí)間: 2025-3-22 07:17
On the Effectiveness of Memory-Based Methods in Machine Learning,g sample to make inferences about novel feature values. The conventional wisdom about nearest neighbor methods is that they are subject to various . and so become infeasible in high dimensional feature spaces. However, recent results such as those by Barron and Jones suggest that these dimensionalit作者: 相容 時(shí)間: 2025-3-22 09:07 作者: 喃喃訴苦 時(shí)間: 2025-3-22 13:14
A Priori Information in Network Design,tions of NNs published in literature dealt with I/O mappings. Recently, however, there has been increased interest in Input — state — Output mapping representation using Dynamic Recurrent Neural Networks (DRNNs) [13–16]. DRNNs are Feed Forward Neural Networks (FFNNs) [17,18] with feedback connection作者: 喃喃訴苦 時(shí)間: 2025-3-22 19:39 作者: 榮幸 時(shí)間: 2025-3-23 00:18
Feature Selection and Classification by a Modified Model with Latent Structure,e of the object is called a class which is denoted by . and takes values in a finite set Ω = {.., ..,..., ..}. An object is described by a .—dimensional vector of features . = (.., ..,..., ..). ∈ . ? ... We wish to build a rule .(.): .. → Ω, which represents one’s guess of a given .. The mapping is 作者: drusen 時(shí)間: 2025-3-23 05:19
Geometric Algebra Based Neural Networks,ral activities compared to the one-dimensional neural activity within the standard neural network framework. Instead of using basically the product of two scalar values they utilise some special algebraic product of two multidimensional quantities. Most of them can be considered as a special type of作者: 格子架 時(shí)間: 2025-3-23 06:16
Discrete Event Complex Systems: Scheduling with Neural Networks, of producing customers’ demands in a timely and economic fashion. A special class, namely flexible manufacturing systems (FMS) has increased in popularity due to its quicker response to market changes, reduction in work-in-process and high levels of productivity [1]. The objective of scheduling is 作者: Ringworm 時(shí)間: 2025-3-23 13:21
Incremental Approximation by Neural Networks,xed architecture which requires us to solve a non-linear optimization problem in a multidimensional parameter space. An alternative approach is to use a . and determine the final set of network parameters in a series of steps, each taking place in a lower dimensional space. There have been considere作者: 懸掛 時(shí)間: 2025-3-23 17:09 作者: A簡(jiǎn)潔的 時(shí)間: 2025-3-23 20:10
Rates of Approximation in a Feedforward Network Depend on the Type of Computational Unit, ([19], [8], [1], [2], [13]). Mhaskar and Micchelli have shown in [22] that a network using any non-polynomial locally Riemann integrable activation can approximate any continuous function of any number of variables on a compact set to any desired degree of accuracy (i.e. it has the universal approx作者: 拱形大橋 時(shí)間: 2025-3-24 02:00
Recent Results and Mathematical Methods for Functional Approximation by Neural Networks,empts to find a parameter vector . such that .|| < ., where . denotes the input-output function produced by a neural network architecture . using as “weights” .. When the input dimension is . and the output dimension is 1, .: R. → .. For example, if . is the standard perceptron architecture with act作者: etidronate 時(shí)間: 2025-3-24 02:26
Differential Neurocontrol of Multidimensional Systems,problems. Learning ability is one of their main advantages, and special learning algorithms provide rather good convergence. They do not require precise initial mathematical models that can be developed during the adaptation process. Generalization properties may ensure solving such situations in th作者: 倒轉(zhuǎn) 時(shí)間: 2025-3-24 08:30
The Psychological Limits of Neural Computation,mputable function is Turing computable. The languages accepted by Turing machines form the recursively enumerable language family .. and, according to the Church-Turing thesis, .. is also the class of algorithmic computable sets. In spite of its generality, the Turing model . solve any problem. Reca作者: Encoding 時(shí)間: 2025-3-24 14:34
Lecture Notes in Computer ScienceRecurrent nets have been introduced in control, computation, signal processing, optimization, and associate memory applications. Given matrices . ∈ ?.., . ∈ ?.., . ∈ ?.., as well as a fixed Lipschitz scalar function . : ? → ?, the . Σ with . and . (., .,.) is given by: .where.: ?. → ?. is the diagonal map 作者: 別名 時(shí)間: 2025-3-24 15:51 作者: incision 時(shí)間: 2025-3-24 19:37
Recurrent Neural Networks: Some Systems-Theoretic Aspects,Recurrent nets have been introduced in control, computation, signal processing, optimization, and associate memory applications. Given matrices . ∈ ?.., . ∈ ?.., . ∈ ?.., as well as a fixed Lipschitz scalar function . : ? → ?, the . Σ with . and . (., .,.) is given by: .where.: ?. → ?. is the diagonal map 作者: 充滿人 時(shí)間: 2025-3-25 02:18
A Brain-Like Design to Learn Optimal Decision Strategies in Complex Environments,In the development of learning systems and neural networks, the issue of complexity occurs at many levels of analysis.作者: 助記 時(shí)間: 2025-3-25 07:19
Approximation of Smooth Functions by Neural Networks,ies ..,..,... is to consider each .. as an unknown fuction of a certain (fixed) number of previous values. A neural network is then trained to approximate this unknown function. We note that one of the reasons for the popularity of neural networks over their precursors, perceptrons, is their universal approximation property.作者: 簡(jiǎn)潔 時(shí)間: 2025-3-25 09:01 作者: 火光在搖曳 時(shí)間: 2025-3-25 12:48
Lecture Notes in Computer Scienceies ..,..,... is to consider each .. as an unknown fuction of a certain (fixed) number of previous values. A neural network is then trained to approximate this unknown function. We note that one of the reasons for the popularity of neural networks over their precursors, perceptrons, is their universal approximation property.作者: 迎合 時(shí)間: 2025-3-25 17:16
Numerical Aspects of?Hyperbolic Geometryr, in many cases, the neural network is treated as a black box, since the internal mathematics of a neural network can be hard to analyse. As the size of a neural network increases, its mathematics becomes more complex and hence harder to analyse. This chapter examines the use of concepts from state作者: Increment 時(shí)間: 2025-3-25 22:57 作者: Feature 時(shí)間: 2025-3-26 01:37
Philipp Andelfinger,Justin N. Kreikemeyercan be viewed as universal approximators of non-linear functions that can learn from examples. This chapter focuses on an iterative algorithm for training neural networks inspired by the strong correspondences existing between NNs and some statistical methods [1][2]. This algorithm is often consider作者: orthopedist 時(shí)間: 2025-3-26 07:52 作者: Irremediable 時(shí)間: 2025-3-26 09:43
https://doi.org/10.1007/978-1-0716-4003-6s probabilistic interpretation depends on the cost function used for training. Consequently, there has been considerable interest in analysing the properties of the mean square error criterion. It has been shown by several authors that, when training a multi-layer neural network by minimizing a mean作者: 鬧劇 時(shí)間: 2025-3-26 13:32 作者: Impugn 時(shí)間: 2025-3-26 20:26 作者: HEED 時(shí)間: 2025-3-26 21:05
Synthesis of?Temporal Causalitye of the object is called a class which is denoted by . and takes values in a finite set Ω = {.., ..,..., ..}. An object is described by a .—dimensional vector of features . = (.., ..,..., ..). ∈ . ? ... We wish to build a rule .(.): .. → Ω, which represents one’s guess of a given .. The mapping is 作者: 取之不竭 時(shí)間: 2025-3-27 05:01
https://doi.org/10.1007/978-3-031-65633-0ral activities compared to the one-dimensional neural activity within the standard neural network framework. Instead of using basically the product of two scalar values they utilise some special algebraic product of two multidimensional quantities. Most of them can be considered as a special type of作者: 思鄉(xiāng)病 時(shí)間: 2025-3-27 06:23
Sota Sato,Jie An,Zhenya Zhang,Ichiro Hasuo of producing customers’ demands in a timely and economic fashion. A special class, namely flexible manufacturing systems (FMS) has increased in popularity due to its quicker response to market changes, reduction in work-in-process and high levels of productivity [1]. The objective of scheduling is 作者: 無王時(shí)期, 時(shí)間: 2025-3-27 09:44 作者: 使殘廢 時(shí)間: 2025-3-27 15:35 作者: 愉快么 時(shí)間: 2025-3-27 19:30
Proactive Real-Time First-Order Enforcement ([19], [8], [1], [2], [13]). Mhaskar and Micchelli have shown in [22] that a network using any non-polynomial locally Riemann integrable activation can approximate any continuous function of any number of variables on a compact set to any desired degree of accuracy (i.e. it has the universal approx作者: overshadow 時(shí)間: 2025-3-28 01:19 作者: 排斥 時(shí)間: 2025-3-28 02:14 作者: Occipital-Lobe 時(shí)間: 2025-3-28 08:23 作者: 顯示 時(shí)間: 2025-3-28 12:52 作者: Guileless 時(shí)間: 2025-3-28 15:13
978-3-540-76160-0Springer-Verlag London Limited 1998作者: 潛伏期 時(shí)間: 2025-3-28 22:22
Perspectives in Neural Computinghttp://image.papertrans.cn/d/image/263965.jpg作者: 極小量 時(shí)間: 2025-3-29 02:02
1431-6854 as. What was not even considered possible a decade or two ago is now not only possible but is also part of everyday practice. As a result, a new approach usually needs to be taken (in order) to get the best out of a situation. What is required is now a computer‘s eye view of the world. However, all 作者: arousal 時(shí)間: 2025-3-29 04:49 作者: inventory 時(shí)間: 2025-3-29 08:15 作者: Chameleon 時(shí)間: 2025-3-29 14:32 作者: pancreas 時(shí)間: 2025-3-29 17:15 作者: 詞匯記憶方法 時(shí)間: 2025-3-29 19:52
Proactive Real-Time First-Order Enforcementweights” .. When the input dimension is . and the output dimension is 1, .: R. → .. For example, if . is the standard perceptron architecture with activation function . hidden units and 1 linear output unit, then for . composed of the v., .. and .. below, one has the usual expression ., where ..∈ .. and .., ..∈ .. are the parameters.作者: 過多 時(shí)間: 2025-3-30 00:27
Conference proceedings‘‘‘‘‘‘‘‘ 2024 the Church-Turing thesis, .. is also the class of algorithmic computable sets. In spite of its generality, the Turing model . solve any problem. Recall, for example, that the halting problem is Turing unsolvable: it is algorithmic undecidable if an arbitrary Turing machine will eventually halt when given some specified, but arbitrary, input.作者: IRATE 時(shí)間: 2025-3-30 07:15 作者: 畏縮 時(shí)間: 2025-3-30 11:42 作者: tariff 時(shí)間: 2025-3-30 13:48 作者: DOTE 時(shí)間: 2025-3-30 17:26 作者: 暗語 時(shí)間: 2025-3-30 21:47
Recent Results and Mathematical Methods for Functional Approximation by Neural Networks,weights” .. When the input dimension is . and the output dimension is 1, .: R. → .. For example, if . is the standard perceptron architecture with activation function . hidden units and 1 linear output unit, then for . composed of the v., .. and .. below, one has the usual expression ., where ..∈ .. and .., ..∈ .. are the parameters.作者: Nomadic 時(shí)間: 2025-3-31 04:51
The Psychological Limits of Neural Computation, the Church-Turing thesis, .. is also the class of algorithmic computable sets. In spite of its generality, the Turing model . solve any problem. Recall, for example, that the halting problem is Turing unsolvable: it is algorithmic undecidable if an arbitrary Turing machine will eventually halt when given some specified, but arbitrary, input.作者: 粗魯?shù)娜?nbsp; 時(shí)間: 2025-3-31 09:03
Numerical Aspects of?Hyperbolic Geometry observed sample, to guess what values an unobserved quantity may have, to predict what values of some quantities will occur, to guess the state of a patient or a technical system, to select values of manipulable variables fed into a controlled system etc.作者: 不舒服 時(shí)間: 2025-3-31 09:25 作者: construct 時(shí)間: 2025-3-31 16:29
Statistical Decision Making and Neural Networks, observed sample, to guess what values an unobserved quantity may have, to predict what values of some quantities will occur, to guess the state of a patient or a technical system, to select values of manipulable variables fed into a controlled system etc.作者: 看法等 時(shí)間: 2025-3-31 19:15
Incremental Approximation by Neural Networks,d various types of such architecture dynamics in which network units or connections are either added or deleted. The simplest type is . where in each step an architecture is extended by adding one new unit.作者: Ordeal 時(shí)間: 2025-3-31 21:51 作者: 忍受 時(shí)間: 2025-4-1 02:07 作者: diabetes 時(shí)間: 2025-4-1 06:18 作者: Adjourn 時(shí)間: 2025-4-1 11:47 作者: GRIPE 時(shí)間: 2025-4-1 18:15
The Use of State Space Control Theory for Analysing Feedforward Neural Networks,lity. Some can be applied completely to feedforward neural networks and others have little or no meaning in the context of neural computing. Each concept will be examined and its used for analysing feedforward neural networks discussed.作者: 者變 時(shí)間: 2025-4-1 19:44
A Priori Information in Network Design,d Nakamura [19] have shown that finite time trajectories of an . dimensional system can be approximated by the states of a Hopfield network, with . output nodes, . hidden nodes, and appropriate initial states.
