The 2011 International Joint Conference on Neural Networks最新文献_第3页

Pulse-Type Hardware Inhibitory Neural Networks for MEMS micro robot using CMOS technology 基于CMOS技术的MEMS微型机器人脉冲型硬件抑制神经网络

The 2011 International Joint Conference on Neural Networks Pub Date : 2011-10-03 DOI: 10.1109/IJCNN.2011.6033416

Ken Saito, K. Okazaki, K. Sakata, T. Ogiwara, Y. Sekine, F. Uchikoba

{"title":"Pulse-Type Hardware Inhibitory Neural Networks for MEMS micro robot using CMOS technology","authors":"Ken Saito, K. Okazaki, K. Sakata, T. Ogiwara, Y. Sekine, F. Uchikoba","doi":"10.1109/IJCNN.2011.6033416","DOIUrl":"https://doi.org/10.1109/IJCNN.2011.6033416","url":null,"abstract":"This paper presents the locomotion generator of MEMS (Micro Electro Mechanical Systems) micro robot. The locomotion generator demonstrates the locomotion of the micro robot, controlled by the P-HINN (Pulse-Type Hardware Inhibitory Neural Networks). P-HINN generates oscillatory patterns of electrical activity such as living organisms. Basic components are the cell body models and inhibitory synaptic models. P-HINN has the same basic features of biological neurons such as threshold, refractory period, spatio-temporal summation characteristics and enables the generation of continuous action potentials. P-HINN was constructed by MOSFETs, can be integrated by CMOS technology. Same as the living organisms P-HINN realized the robot control without using any software programs, or A/D converters. The size of the micro robot fabricated by the MEMS technology was 4×4×3.5 mm. The frame of the robot was made of silicon wafer, equipped with the rotary type actuators, the link mechanisms and 6 legs. The MEMS micro robot emulated the locomotion method and the neural networks of the insect by the rotary type actuators, link mechanisms and P-HINN. As a result, we show that P-HINN can control the forward and backward locomotion of fabricated MEMS micro robot, and also switched the direction by inputting the external trigger pulse. The locomotion speed was 19.5 mm/min and the step width was 1.3 mm.","PeriodicalId":415833,"journal":{"name":"The 2011 International Joint Conference on Neural Networks","volume":"35 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":"115090626","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: 2

Discovery of pattern meaning from delayed rewards by reinforcement learning with a recurrent neural network 用递归神经网络强化学习从延迟奖励中发现模式意义

The 2011 International Joint Conference on Neural Networks Pub Date : 2011-10-03 DOI: 10.1109/IJCNN.2011.6033394

K. Shibata, Hiroki Utsunomiya

{"title":"Discovery of pattern meaning from delayed rewards by reinforcement learning with a recurrent neural network","authors":"K. Shibata, Hiroki Utsunomiya","doi":"10.1109/IJCNN.2011.6033394","DOIUrl":"https://doi.org/10.1109/IJCNN.2011.6033394","url":null,"abstract":"In this paper, by the combination of reinforcement learning and a recurrent neural network, the authors try to provide an explanation for the question: why humans can discover the meaning of patterns and acquire appropriate behaviors based on it. Using a system with a real movable camera, it is demonstrated in a simple task in which the system discovers pattern meaning from delayed rewards by reinforcement learning with a recurrent neural network. When the system moves its camera to the direction of an arrow presented on a display, it can get a reward. One kind of arrow is chosen randomly among four kinds at each episode, and the input of the network is 1,560 visual signals from the camera. After learning, the system could move its camera to the arrow direction. It was found that some hidden neurons represented the arrow direction not depending on the presented arrow pattern and kept it after the arrow disappeared from the image, even though no arrow was seen when it was rewarded and no one told the system that the arrow direction is important to get the reward. Generalization to some new arrow patterns and associative memory function also can be seen to some extent.","PeriodicalId":415833,"journal":{"name":"The 2011 International Joint Conference on Neural Networks","volume":"18 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":"115201820","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: 18

Class of all i-v dynamics for memristive elements in pattern recognition systems 模式识别系统中记忆元素的所有i-v动力学类

The 2011 International Joint Conference on Neural Networks Pub Date : 2011-10-03 DOI: 10.1109/IJCNN.2011.6033514

F. Corinto, A. Ascoli, M. Gilli

引用次数: 9

On retrieval performance of associative memory by Complex-valued Synergetic Computer 复值协同计算机对联想记忆检索性能的研究

The 2011 International Joint Conference on Neural Networks Pub Date : 2011-10-03 DOI: 10.1109/IJCNN.2011.6033383

M. Kimura, T. Isokawa, H. Nishimura, N. Matsui

引用次数: 1

The impact of preprocessing on forecasting electrical load: An empirical evaluation of segmenting time series into subseries 预处理对电力负荷预测的影响:时间序列分割成子序列的实证评价

The 2011 International Joint Conference on Neural Networks Pub Date : 2011-10-03 DOI: 10.1109/IJCNN.2011.6033657

S. Crone, N. Kourentzes

{"title":"The impact of preprocessing on forecasting electrical load: An empirical evaluation of segmenting time series into subseries","authors":"S. Crone, N. Kourentzes","doi":"10.1109/IJCNN.2011.6033657","DOIUrl":"https://doi.org/10.1109/IJCNN.2011.6033657","url":null,"abstract":"Forecasting future electricity load represents one of the most prominent areas of electrical engineering, in which artificial neural networks (NN) are routinely applied in practice. The common approach to overcome the complexity of building NNs for high-frequency load data is to segment the time series into simpler and more homogeneous subseries, e.g. seven subseries of hourly loads of only Mondays, Tuesdays etc. These are forecasted independently, using a separate NN model, and then recombined to provide a complete trace forecast for the next days ahead. Despite the empirical importance of load forecasting, and the high operational cost associated with forecast errors, the potential benefits of segmenting time series into subseries have not been evaluated in an empirical comparison. This paper assesses the accuracy of segmenting continuous time series into daily subseries, versus forecasting the original, continuous time series with NNs. Accuracy on hourly UK load data is provided in a valid experimental design, using multiple rolling time origins and robust error metrics in comparison to statistical benchmark algorithms. Results indicate the superior performance of NN on continuous, non-segmented time series, in contrast to best practices in research, practice and software implementations.","PeriodicalId":415833,"journal":{"name":"The 2011 International Joint Conference on Neural Networks","volume":"6 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":"121874768","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

Two-phase GA parameter tunning method of CPGs for quadruped gaits 四足步态CPGs的两相遗传算法参数整定方法

The 2011 International Joint Conference on Neural Networks Pub Date : 2011-10-03 DOI: 10.1109/IJCNN.2011.6033438

J. H. Barrón-Zambrano, C. Torres-Huitzil

{"title":"Two-phase GA parameter tunning method of CPGs for quadruped gaits","authors":"J. H. Barrón-Zambrano, C. Torres-Huitzil","doi":"10.1109/IJCNN.2011.6033438","DOIUrl":"https://doi.org/10.1109/IJCNN.2011.6033438","url":null,"abstract":"Nowadays, the locomotion control research field has been pretty active and has produced different approaches for legged robots. From biological studies, it is known that fundamental rhythmic periodical signals for locomotion are produced by Central Pattern Generator (CPG) and the main part of the coordination takes place in the central nervous system. In spite of the CPG-utility, there are few training methodologies to generate the rhythmic signals based in CPG models. In this paper, an automatic method to find the synaptic weights to generate three basic gaits using Genetic Algorithms (GA) is presented. The method is based on the analysis of the oscillator behavior and its interactions with other oscillators, in a network. The oscillator model used in this work is the proposed by Van Der Pol (VDP). A two-phase GA is adapted: (i) to find the parameter values to produce oscillations and (ii) to generate the weight values of the interconnections between oscillators. The results show the feasibility of the presented method to find the parameters to generate different gaits. The implementation takes advantage that the fitness function works directly with the oscillator and the network. So, knowledge about the robot dynamic is not necessary. The GA based approach uses small population and limited numbers of generations, ideal to be processed on either computers with reduced resources or hardware implementations.","PeriodicalId":415833,"journal":{"name":"The 2011 International Joint Conference on Neural Networks","volume":"16 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":"129843984","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: 13

Graph weighted subspace learning models in bankruptcy 破产中的图加权子空间学习模型

The 2011 International Joint Conference on Neural Networks Pub Date : 2011-10-03 DOI: 10.1109/IJCNN.2011.6033479

B. Ribeiro, Ningshan Chen

引用次数: 11

Toward constructive methods for sigmoidal neural networks - function approximation in engineering mechanics applications s型神经网络的构造方法——函数逼近在工程力学中的应用

The 2011 International Joint Conference on Neural Networks Pub Date : 2011-10-03 DOI: 10.1109/IJCNN.2011.6033546

Jin‐Song Pei, J. P. Wright, S. Masri, E. Mai, A. Smyth

{"title":"Toward constructive methods for sigmoidal neural networks - function approximation in engineering mechanics applications","authors":"Jin‐Song Pei, J. P. Wright, S. Masri, E. Mai, A. Smyth","doi":"10.1109/IJCNN.2011.6033546","DOIUrl":"https://doi.org/10.1109/IJCNN.2011.6033546","url":null,"abstract":"This paper reports a continuous development of the work by the authors presented at IJCNN 2005 & 2007 [1, 2]. A series of parsimonious universal approximator architectures with pre-defined values for weights and biases called “neural network prototypes” are proposed and used in a repetitive and systematic manner for the initialization of sigmoidal neural networks in function approximation. This paper provides a more in-depth literature review, presents one training example using laboratory data indicating quick convergence and trained sigmoidal neural networks with stable generalization capability, and discusses the complexity measure in [3, 4]. This study centers on approximating a subset of static nonlinear target functions - mechanical restoring force considered as a function of system states (displacement and velocity) for single-degree-of-freedom systems. We strive for efficient and rigorous constructive methods for sigmoidal neural networks to solve function approximation problems in this engineering mechanics application and beyond. Future work is identified.","PeriodicalId":415833,"journal":{"name":"The 2011 International Joint Conference on Neural Networks","volume":"60 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":"121438073","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: 3

Unsupervised features extraction from asynchronous silicon retina through Spike-Timing-Dependent Plasticity 基于峰值时间依赖可塑性的非监督特征提取

The 2011 International Joint Conference on Neural Networks Pub Date : 2011-10-03 DOI: 10.1109/IJCNN.2011.6033311

O. Bichler, D. Querlioz, S. Thorpe, J. Bourgoin, C. Gamrat

引用次数: 40

Residential energy system control and management using adaptive dynamic programming 住宅能源系统的自适应动态规划控制与管理

The 2011 International Joint Conference on Neural Networks Pub Date : 2011-10-03 DOI: 10.1109/IJCNN.2011.6033209

Ting Huang, Derong Liu

引用次数: 48