The 2011 International Joint Conference on Neural Networks最新文献_第4页

Reinforcement active learning hierarchical loops 强化主动学习分层循环

The 2011 International Joint Conference on Neural Networks Pub Date : 2011-10-03 DOI: 10.1109/IJCNN.2011.6033617

Goren Gordon, E. Ahissar

{"title":"Reinforcement active learning hierarchical loops","authors":"Goren Gordon, E. Ahissar","doi":"10.1109/IJCNN.2011.6033617","DOIUrl":"https://doi.org/10.1109/IJCNN.2011.6033617","url":null,"abstract":"A curious agent, be it a robot, animal or human, acts so as to learn as much as possible about itself and its environment. Such an agent can also learn without external supervision, but rather actively probe its surrounding and autonomously induce the relations between its action's effects on the environment and the resulting sensory input. We present a model of hierarchical motor-sensory loops for such an autonomous active learning agent, meaning a model that selects the appropriate action in order to optimize the agent's learning. Furthermore, learning one motor-sensory mapping enables the learning of other mappings, thus increasing the extent and diversity of knowledge and skills, usually in hierarchical manner. Each such loop attempts to optimally learn a specific correlation between the agent's available internal information, e.g. sensory signals and motor efference copies, by finding the action that optimizes that learning. We demonstrate this architecture on the well-studied vibrissae system, and show how sensory-motor loops are actively learnt from the bottom-up, starting with the forward and inverse models of whisker motion and then extending them to object localization. The model predicts transition from free-air whisking that optimally learns the self-generated motor-sensory mapping to touch-induced palpation that optimizes object localization, both observed in naturally behaving rats.","PeriodicalId":415833,"journal":{"name":"The 2011 International Joint Conference on Neural Networks","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114798272","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 16

Lag selection for time series forecasting using Particle Swarm Optimization 基于粒子群算法的时间序列预测滞后选择

The 2011 International Joint Conference on Neural Networks Pub Date : 2011-10-03 DOI: 10.1109/IJCNN.2011.6033535

Gustavo H. T. Ribeiro, P. S. D. M. Neto, George D. C. Cavalcanti, Ing Ren Tsang

{"title":"Lag selection for time series forecasting using Particle Swarm Optimization","authors":"Gustavo H. T. Ribeiro, P. S. D. M. Neto, George D. C. Cavalcanti, Ing Ren Tsang","doi":"10.1109/IJCNN.2011.6033535","DOIUrl":"https://doi.org/10.1109/IJCNN.2011.6033535","url":null,"abstract":"The time series forecasting is an useful application for many areas of knowledge such as biology, economics, climatology, biology, among others. A very important step for time series prediction is the correct selection of the past observations (lags). This paper uses a new algorithm based in swarm of particles to feature selection on time series, the algorithm used was Frankenstein's Particle Swarm Optimization (FPSO). Many forms of filters and wrappers were proposed to feature selection, but these approaches have their limitations in relation to properties of the data set, such as size and whether they are linear or not. Optimization algorithms, such as FPSO, make no assumption about the data and converge faster. Hence, the FPSO may to find a good set of lags for time series forecasting and produce most accurate forecastings. Two prediction models were used: Multilayer Perceptron neural network (MLP) and Support Vector Regression (SVR). The results show that the approach improved previous results and that the forecasting using SVR produced best results, moreover its showed that the feature selection with FPSO was better than the features selection with original Particle Swarm Optimization.","PeriodicalId":415833,"journal":{"name":"The 2011 International Joint Conference on Neural Networks","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126478815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 30

Solving Traveling Salesman Problem by a hybrid combination of PSO and Extremal Optimization 用粒子群优化与极值优化的混合组合求解旅行商问题

The 2011 International Joint Conference on Neural Networks Pub Date : 2011-10-03 DOI: 10.1109/IJCNN.2011.6033402

Saeed Khakmardan, H. Poostchi, M. Akbarzadeh-T.

{"title":"Solving Traveling Salesman Problem by a hybrid combination of PSO and Extremal Optimization","authors":"Saeed Khakmardan, H. Poostchi, M. Akbarzadeh-T.","doi":"10.1109/IJCNN.2011.6033402","DOIUrl":"https://doi.org/10.1109/IJCNN.2011.6033402","url":null,"abstract":"Particle Swarm Optimization (PSO) has received great attention in recent years as a successful global search algorithm, due to its simple implementation and inexpensive computation overhead. However, PSO still suffers from the problem of early convergence to locally optimal solutions. Extremal Optimization (EO) is a local search algorithm that has been able to solve NP hard optimization problems. The combination of PSO with EO benefits from the exploration ability of PSO and the exploitation ability of EO, and reduces the probability of early trapping in the local optima. In other words, due to the EO's strong local search capability, the PSO focuses on its global search by a new mutation operator that prevents loss of variety among the particles. This is done when the particle's parameters exceed the problem conditions. The resulting hybrid algorithm Mutated PSO-EO (MPSO-EO) is then applied to the Traveling Salesman Problem (TSP) as a NP hard multimodal optimization problem. The performance of the proposed approach is compared with several other metaheuristic methods on 3 well known TSP databases and 10 unimodal and multimodal benchmark functions.","PeriodicalId":415833,"journal":{"name":"The 2011 International Joint Conference on Neural Networks","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128078875","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Fast AdaBoost training using weighted novelty selection 快速AdaBoost训练使用加权新颖性选择

The 2011 International Joint Conference on Neural Networks Pub Date : 2011-10-03 DOI: 10.1109/IJCNN.2011.6033366

Mojtaba Seyedhosseini, António R. C. Paiva, T. Tasdizen

引用次数: 23

Modularity adaptation in cooperative coevolution of feedforward neural networks 前馈神经网络协同进化中的模块化适应

The 2011 International Joint Conference on Neural Networks Pub Date : 2011-10-03 DOI: 10.1109/IJCNN.2011.6033287

Rohitash Chandra, Marcus Frean, Mengjie Zhang

引用次数: 11

A reversibility analysis of encoding methods for spiking neural networks 尖峰神经网络编码方法的可逆性分析

The 2011 International Joint Conference on Neural Networks Pub Date : 2011-10-03 DOI: 10.1109/IJCNN.2011.6033443

Cameron Johnson, Sinchan Roychowdhury, G. Venayagamoorthy

引用次数: 9

Generation of composed musical structures through recurrent neural networks based on chaotic inspiration 基于混沌灵感的递归神经网络合成音乐结构

The 2011 International Joint Conference on Neural Networks Pub Date : 2011-10-03 DOI: 10.1109/IJCNN.2011.6033648

Andres Eduardo Coca Salazar, R. Romero, Liang Zhao

引用次数: 21

Models of clifford recurrent neural networks and their dynamics clifford递归神经网络模型及其动力学

The 2011 International Joint Conference on Neural Networks Pub Date : 2011-10-03 DOI: 10.1109/IJCNN.2011.6033336

Y. Kuroe

引用次数: 43

A batch self-organizing maps algorithm based on adaptive distances 一种基于自适应距离的批量自组织映射算法

The 2011 International Joint Conference on Neural Networks Pub Date : 2011-10-03 DOI: 10.1109/IJCNN.2011.6033515

L. Pacífico, F. D. Carvalho

引用次数: 2

Visually-guided adaptive robot (ViGuAR) 视觉引导自适应机器人(vigar)

The 2011 International Joint Conference on Neural Networks Pub Date : 2011-10-03 DOI: 10.1109/IJCNN.2011.6033608

Gennady Livitz, Heather Ames, Ben Chandler, A. Gorchetchnikov, Jasmin Léveillé, Zlatko Vasilkoski, Massimiliano Versace, E. Mingolla, G. Snider, R. Amerson, Dick Carter, H. Abdalla, M. Qureshi

引用次数: 2