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Advances in Neural Networks: 4th International Symposium on Neural Networks, ISNN 2007, Nanjing, China, June 3-7, 2007, Proceedings, Part I

Derong Liu ; Shumin Fei ; Zeng-Guang Hou ; Huaguang Zhang ; Changyin Sun (eds.)

En conferencia: 4º International Symposium on Neural Networks (ISNN) . Nanjing, China . June 3, 2007 - June 7, 2007

Resumen/Descripción – provisto por la editorial

No disponible.

Palabras clave – provistas por la editorial

Artificial Intelligence (incl. Robotics); Computation by Abstract Devices; Computer Communication Networks; Algorithm Analysis and Problem Complexity; Discrete Mathematics in Computer Science; Pattern Recognition

Disponibilidad
Institución detectada Año de publicación Navegá Descargá Solicitá
No detectada 2007 SpringerLink

Información

Tipo de recurso:

libros

ISBN impreso

978-3-540-72382-0

ISBN electrónico

978-3-540-72383-7

Editor responsable

Springer Nature

País de edición

Reino Unido

Fecha de publicación

Información sobre derechos de publicación

© Springer-Verlag Berlin Heidelberg 2007

Tabla de contenidos

The Application of Adaptive Critic Design in the Nosiheptide Fermentation

Dapeng Zhang; Aiguo Wu; Fuli Wang; Zhiling Lin

An adaptive critic design is used in the nosiheptide fermentation process to solve the intractable optimization problem. The utility function is defined as the increment of biomass concentration at the adjacent intervals. The state variables are chosen as the biomass concentration, the substrate concentration, the dissolved oxygen concentration and the inhibition concentration. The decision variables are chosen as the temperature, the stirring speed, the airflow and the tank pressure. The adaptive critic method determines optimal control laws for a system by successively adapting the critic networks and the action network. The simulation shows at the same initial conditions this technique can make the fermentation shorten 6 hours.

- Adaptive Dynamic Programming and Reinforcement Learning | Pp. 380-386

On-Line Learning Control for Discrete Nonlinear Systems Via an Improved ADDHP Method

Huaguang Zhang; Qinglai Wei; Derong Liu

This paper mainly discusses a generic scheme for on-line adaptive critic design for nonlinear system based on neural dynamic programming (NDP), more exactly, an improved action-depended dual heuristic dynamic programming (ADDHP) method. The principal merit of the proposed method is to avoid the model neural network which predicts the state of next time step, and only use current and previous states in the method, as makes the algorithm more suitable for real-time or on-line application for process control. In this paper, convergence proof of the method will also be given to guarantee the control to reach the optimal. At last, simulation result verifies the performance.

- Adaptive Dynamic Programming and Reinforcement Learning | Pp. 387-396

Reinforcement Learning Reward Functions for Unsupervised Learning

Colin Fyfe; Pei Ling Lai

We extend a reinforcement learning algorithm, REINFORCE [13] which has previously been used to cluster data [10]. By using base Gaussian learners, we extend the method so that it can perform a variety of unsupervised learning tasks such as principal component analysis, exploratory projection pursuit and canonical correlation analysis.

- Adaptive Dynamic Programming and Reinforcement Learning | Pp. 397-402

A Hierarchical Learning System Incorporating with Supervised, Unsupervised and Reinforcement Learning

Jinglu Hu; Takafumi Sasakawa; Kotaro Hirasawa; Huiru Zheng

According to Hebb’s , the brain has the capability of function localization. On the other hand, it is suggested that the brain has three different learning paradigms: supervised, unsupervised and reinforcement learning. Inspired by the above knowledge of brain, we present a hierarchical learning system consisting of three parts: supervised learning (SL) part, unsupervised learning (UL) part and reinforcement learning (RL) part. The SL part is a main part learning input-output mapping; the UL part realizes the function localization of learning system by controlling firing strength of neurons in SL part based on input patterns; the RL part optimizes system performance by adjusting parameters in UL part. Simulation results confirm the effectiveness of the proposed hierarchical learning system.

- Adaptive Dynamic Programming and Reinforcement Learning | Pp. 403-412

Enclosing Machine Learning for Class Description

Xunkai Wei; Johan Löfberg; Yue Feng; Yinghong Li; Yufei Li

A novel machine learning paradigm, i.e. enclosing machine learning based on regular geometric shapes was proposed. It adopted regular minimum volume enclosing and bounding geometric shapes (sphere, ellipsoid, and box) or their unions and so on to obtain one class description model and thus imitate the human “Cognizing” process. A point detection and assignment algorithm based on the one class description model was presented to imitate the human “Recognizing” process. To illustrate the concept and algorithm, a minimum volume enclosing ellipsoid (MVEE) strategy for enclosing machine learning was investigated in detail. A regularized minimum volume enclosing ellipsoid problem and dual form were presented due to probable existence of zero eigenvalues in regular MVEE problem. To solve the high dimensional one class description problem, the MVEE in kernel defined feature space was presented. A corresponding dual form and kernelized Mahalanobis distance formula was presented. We investigated the performance of the enclosing learning machine via benchmark datasets and compared with support vector machines (SVM).

- Adaptive Dynamic Programming and Reinforcement Learning | Pp. 424-433

An Extremely Simple Reinforcement Learning Rule for Neural Networks

Xiaolong Ma

In this paper we derive a simple reinforcement learning rule based on a more general form of REINFORCE formulation. We test our new rule on both classification and reinforcement problems. The results have shown that although this simple learning rule has a high probability of being stuck in local optimum for the case of classification tasks, it is able to solve some global reinforcement problems (e.g. the cart-pole balancing problem) directly in the continuous space.

- Adaptive Dynamic Programming and Reinforcement Learning | Pp. 434-440

Long-Term Electricity Demand Forecasting Using Relevance Vector Learning Mechanism

Zhi-gang Du; Lin Niu; Jian-guo Zhao

In electric power system, long term peak load forecasting plays an important role in terms of policy planning and budget allocation. The planning of power system expansion project starts with the forecasting of anticipated load requirement. Accurate forecasting method can be helpful in developing power supply strategy and development plan, especially for developing countries where the demand is increased with dynamic and high growth rate. This paper proposes a peak load forecasting model using relevance vector machine (RVM), which is based on a probabilistic Bayesian learning framework with an appropriate prior that results in a sparse representation. The most compelling feature of the RVM is, while capable of generalization performance comparable to an equivalent support vector machine (SVM), that it typically utilizes dramatically fewer kernel functions. The proposed method has been tested on a practical power system, and the result indicates the effectiveness of such forecasting model.

- Adaptive Dynamic Programming and Reinforcement Learning | Pp. 465-472

An IP and GEP Based Dynamic Decision Model for Stock Market Forecasting

Yuehui Chen; Qiang Wu; Feng Chen

The forecasting models for stock market index using computational intelligence such as Artificial Neural networks(ANNs) and Genetic programming(GP), especially hybrid Immune Programming (IP) Algorithm and Gene Expression Programming(GEP) have achieved favorable results. However, these studies, have assumed a static environment. This study investigates the development of a new dynamic decision forecasting model. Application results prove the higher precision and generalization capacity of the predicting model obtained by the new method than static models.

- Adaptive Dynamic Programming and Reinforcement Learning | Pp. 473-479

Application of Neural Network on Rolling Force Self-learning for Tandem Cold Rolling Mills

Jingming Yang; Haijun Che; Fuping Dou; Shuhui Liu

All the factors that influence the rolling force are analyzed, and the neural network model which uses the back propagation (BP) learning algorithm for the calculation of rolling force is created. The initial network’s weights corresponding to the input material grades are taught by the traditional theoretical model, and saved in the database. In order to increase the prediction accuracy of rolling force, we use the measured rolling force data to teach the neural network after several coils of the same input material are rolled down.

- Adaptive Dynamic Programming and Reinforcement Learning | Pp. 480-486

A Hybrid Knowledge-Based Neural-Fuzzy Network Model with Application to Alloy Property Prediction

Min-You Chen; Quandi Wang; Yongming Yang

This paper presents a hybrid modeling method which incorporates knowledge-based components elicited from human expertise into underlying data-driven neural-fuzzy network models. Two different methods in which both measured data and a priori knowledge are incorporated into the model building process are discussed. Based on the combination of fuzzy logic and neural networks, a simple and effective knowledge-based neural-fuzzy network model has been developed and applied to the impact toughness prediction of alloy steels. Simulation results show that the model performance can be improved by incorporating expert knowledge into existing neural-fuzzy models.

- Neural Networks for Nonlinear Systems Modeling | Pp. 528-535