By Xiaolin Hu, Yousheng Xia, Yunong Zhang, Dongbin Zhao

The quantity LNCS 9377 constitutes the refereed complaints of the twelfth overseas Symposium on Neural Networks, ISNN 2015, held in jeju, South Korea on October 2015. The fifty five revised complete papers provided have been rigorously reviewed and chosen from ninety seven submissions. those papers conceal many issues of neural network-related learn together with clever regulate, neurodynamic research, memristive neurodynamics, desktop imaginative and prescient, sign processing, computing device studying, and optimization.

Show description

Read or Download Advances in Neural Networks – ISNN 2015: 12th International Symposium on Neural Networks, ISNN 2015, Jeju, South Korea, October 15–18, 2015, Proceedings PDF

Similar data mining books

Data Visualization: Part 1, New Directions for Evaluation, Number 139

Do you converse information and knowledge to stakeholders? This factor is an element 1 of a two-part sequence on facts visualization and assessment. partly 1, we introduce contemporary advancements within the quantitative and qualitative facts visualization box and supply a old point of view on facts visualization, its capability position in assessment perform, and destiny instructions.

Big Data Imperatives: Enterprise Big Data Warehouse, BI Implementations and Analytics

Titanic info Imperatives, specializes in resolving the main questions about everyone’s brain: Which info concerns? Do you have got sufficient info quantity to justify the utilization? the way you are looking to procedure this volume of information? How lengthy do you actually need to maintain it energetic to your research, advertising, and BI purposes?

Learning Analytics in R with SNA, LSA, and MPIA

This publication introduces significant Purposive interplay research (MPIA) thought, which mixes social community research (SNA) with latent semantic research (LSA) to aid create and examine a significant studying panorama from the electronic lines left by way of a studying group within the co-construction of information.

Metadata and Semantics Research: 10th International Conference, MTSR 2016, Göttingen, Germany, November 22-25, 2016, Proceedings

This booklet constitutes the refereed court cases of the tenth Metadata and Semantics study convention, MTSR 2016, held in Göttingen, Germany, in November 2016. The 26 complete papers and six brief papers provided have been conscientiously reviewed and chosen from sixty seven submissions. The papers are geared up in different classes and tracks: electronic Libraries, info Retrieval, associated and Social facts, Metadata and Semantics for Open Repositories, examine info structures and knowledge Infrastructures, Metadata and Semantics for Agriculture, meals and surroundings, Metadata and Semantics for Cultural Collections and purposes, eu and nationwide tasks.

Additional info for Advances in Neural Networks – ISNN 2015: 12th International Symposium on Neural Networks, ISNN 2015, Jeju, South Korea, October 15–18, 2015, Proceedings

Example text

In [5], policy iteration algorithm for discrete-time nonlinear systems was developed. For many traditional iterative ADP algorithms, they require to build the model of nonlinear systems and then perform the ADP algorithms to derive an improved control policy [11, 16, 18–22, 24, 27, 28]. In contrast, Q-learning, proposed by Watkins [14, 15], is a typical data-based ADP algorithm. In [10], Q-learning was named action-dependent heuristic dynamic programming (ADHDP). For Q-learning algorithms, Q functions are used instead of value functions in This work was supported in part by the National Natural Science Foundation of China under Grants 61273140, 61304086, 61374105, and 61233001, and in part by Beijing Natural Science Foundation under Grant 4132078.

It is known that there exists a unique solution x(t, ξ ) on t ≥ 0 with initial data ξ ∈ CFb0 ([−τ ,0], Rn ) . Moreover, both f ( x, y, t ) and σ ( x, y, t ) are locally bounded in ( x, y) and uniformly bounded in t . For each V ∈ C 2,1 ( R n × R+ ; R+ ) , we define an operator LV from R n × R n × R+ to R by LV = ∂V / ∂t + ∂V / ∂x ⋅ f + 1/ 2trace[σ T (∂ 2V / ∂xi ∂x j )σ ] (3) where ∂V / ∂z = (∂V / ∂z1 ,…, ∂V / ∂zn ) . (Invariance principle [20]) Assume that there are functions 1 n V ∈ C ( R × R+ ; R+ ) , β ∈ L ( R+ , R+ ) and ω1 , ω2 ∈ C ( R , R+ ) such that Lemma 2,1 1.

Assumption 4. The utility function U (xk , uk ) is a continuous positive definite function of xk and uk . A New Discrete-Time Iterative Adaptive Dynamic Programming Algorithm 45 Define the control sequence set as Uk = uk : uk = (uk , uk+1 , . ), ∀uk+i ∈ Rm , i = 0, 1, . . Then, for a control sequence uk ∈ Uk , the optimal performance index function is defined as J ∗ (xk ) = min J(xk , uk ) : uk ∈ Uk . uk (3) According to [14] and [15], the optimal Q function satisfies the Q-Bellman equation Q∗ (xk , uk ) = U (xk , uk ) + min Q∗ (xk+1 , uk+1 ).

Download PDF sample

Rated 4.51 of 5 – based on 6 votes