Self-evolutional neural network knowledge base

In this article, we have applied cognition on robot using Q-learning based situation operator model. The situation operator model takes the initial situation of the mobile robot and applies a set of operators in order to move the robot to the destination. The initial situation of the mobile robot is defined by a set of characteristics inferred by the sensor inputs. The Situation-Operator Model (SOM) model comprises of a planning and learning module which uses certain heuristics for learning through the mobile robot and a knowledge base which stored the experiences of the mobile robot. The control and learning of the robot is done using q-learning. A camera sensor and an ultrasonic sensor were used as the sensory inputs for the mobile robot. These sensory inputs are used to define the initial situation, which is then used in the learning module to apply the valid operator. The results obtained by the proposed method were compared to the result obtained by Reinforcement-Based Artificial Neural Network for path planning.

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Construction and Functions of Self-Study System for Power Plant Operation

Volume 1: 20th Computers and Information in Engineering Conference ◽

10.1115/detc2000/cie-14596 ◽

2000 ◽

Author(s):

Shengli Tang ◽

Zuwei He ◽

Tao Chang ◽

Liming Xuan

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Expert System ◽

Power Plant ◽

Knowledge Base ◽

Practical Experience ◽

Plant Operation ◽

Self Study ◽

Power Plant Operation ◽

Artificial Neural

Abstract In this paper, the Construction and functions of the self-study system for power plant operation is introduced. As a self-study system, it consists of two parts, a simulator and knowledge base. The knowledge base has been built by the combination of expert system and artificial neural network, which supports the system with practical experience and theoretic knowledge. The trainees’ knowledge can be improved by using the system. The realization of the intelligent training function, applications of expert system and artificial neural network are mainly introduced in this paper.

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Learning from Approximate Human Decisions by a Robot

Journal of Robotics and Mechatronics ◽

10.20965/jrm.2007.p0068 ◽

2007 ◽

Vol 19 (1) ◽

pp. 68-76 ◽

Cited By ~ 1

Author(s):

Chandimal Jayawardena ◽

◽

Keigo Watanabe ◽

Kiyotaka Izumi

Keyword(s):

Neural Network ◽

Decision Making ◽

Natural Language ◽

Knowledge Base ◽

Probabilistic Neural Network ◽

Robotic Manipulator ◽

Human Aspects ◽

Pick And Place ◽

Robot Systems ◽

Related Aspect

Robot systems operating under natural-language commands must be able to infer the meaning intended by the issuer. Despite some successful research, however, an important related aspect not yet addressed has been the possibility of learning from natural-language commands. Such commands, generated by human users, contain valuable information. The inherent subjectivity of natural language, however, complicates potential learning from such commands and their interpretation. We propose decision making for robots operating under natural-language commands influenced by human aspects of decision making. Under our proposed concept, demonstrated in experiments conducted using a robotic manipulator, the robot is controlled using natural-language commands to conduct pick-and-place operations, during which the robot builds a knowledge base. After this learning, which uses a probabilistic neural network, the robot conducts similar tasks based on approximate decisions from the knowledge gained.

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A neural network for fast inferencing on a fuzzy knowledge base

[1992] Proceedings. The Sixteenth Annual International Computer Software and Applications Conference ◽

10.1109/cmpsac.1992.217577 ◽

2003 ◽

Author(s):

K.S. Kumar ◽

M. Sparancia ◽

A. Unnikrishnan

Keyword(s):

Neural Network ◽

Knowledge Base ◽

Fuzzy Knowledge

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Research on Creep Constitutive Model of TC11 Titanium Alloy Based on RBFNN

Materials Science Forum ◽

10.4028/www.scientific.net/msf.575-578.1050 ◽

2008 ◽

Vol 575-578 ◽

pp. 1050-1055 ◽

Cited By ~ 4

Author(s):

Xiang Hua Peng ◽

Ying She Luo ◽

Jing Ye Zhou ◽

Min Yu ◽

Tao Luo

Keyword(s):

Neural Network ◽

Titanium Alloy ◽

Knowledge Base ◽

Rbf Neural Network ◽

Creep Testing ◽

Primary Model ◽

Constitutive Mode ◽

Creep Constitutive Model ◽

Tc11 Titanium Alloy ◽

Rheological Forming

The paper is aimed to exploit a creep constitutive mode of TC11 titanium alloy based on RBF neural network. Creep testing data of TC11 titanium alloy obtained under the same temperature and different stress are considered as knowledge base and the characteristics of rheological forming of materials and radial basis function neural network (RBFNN) are also combined when exploiting the model. A part of data extracted from knowledge base is divided into two groups: one is learning sample and the other testing sample, which are being performed training, learning and simulating. Then predicting value is compared with the creep testing value and the theoretical value deduced by primary model, which validates that the RBFNN model has higher precision and generalizing ability.

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Knowledge Transfer for Out-of-Knowledge-Base Entities : A Graph Neural Network Approach

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2017/250 ◽

2017 ◽

Cited By ~ 26

Author(s):

Takuo Hamaguchi ◽

Hidekazu Oiwa ◽

Masashi Shimbo ◽

Yuji Matsumoto

Keyword(s):

Neural Network ◽

Knowledge Base ◽

State Of The Art ◽

Test Time ◽

Network Approach ◽

Missing Information ◽

Neural Network Approach ◽

Training Time ◽

Proposed Model ◽

Graph Neural Networks

Knowledge base completion (KBC) aims to predict missing information in a knowledge base. In this paper, we address the out-of-knowledge-base (OOKB) entity problem in KBC: how to answer queries concerning test entities not observed at training time. Existing embedding-based KBC models assume that all test entities are available at training time, making it unclear how to obtain embeddings for new entities without costly retraining. To solve the OOKB entity problem without retraining, we use graph neural networks (Graph-NNs) to compute the embeddings of OOKB entities, exploiting the limited auxiliary knowledge provided at test time. The experimental results show the effectiveness of our proposed model in the OOKB setting. Additionally, in the standard KBC setting in which OOKB entities are not involved, our model achieves state-of-the-art performance on the WordNet dataset.

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Vehicle Trajectory Prediction by Knowledge-Driven LSTM Network in Urban Environments

Journal of Advanced Transportation ◽

10.1155/2020/8894060 ◽

2020 ◽

Vol 2020 ◽

pp. 1-20

Author(s):

Shaobo Wang ◽

Pan Zhao ◽

Biao Yu ◽

Weixin Huang ◽

Huawei Liang

Keyword(s):

Neural Network ◽

Knowledge Base ◽

Prior Knowledge ◽

Prediction Models ◽

Short Term Memory ◽

A Priori ◽

Urban Environments ◽

Intelligent Vehicles ◽

Trajectory Prediction ◽

Reasoning System

An accurate prediction of future trajectories of surrounding vehicles can ensure safe and reasonable interaction between intelligent vehicles and other types of vehicles. Vehicle trajectories are not only constrained by a priori knowledge about road structure, traffic signs, and traffic rules but also affected by posterior knowledge about different driving styles of drivers. The existing prediction models cannot fully combine the prior and posterior knowledge in the driving scene and perform well only in a specific traffic scenario. This paper presents a long short-term memory (LSTM) neural network driven by knowledge. First, a driving knowledge base is constructed to describe the prior knowledge about a driving scenario. Then, the prediction reference baseline (PRB) based on driving knowledge base is determined by using the rule-based online reasoning system. Finally, the future trajectory of the target vehicle is predicted by an LSTM neural network based on the prediction reference baseline, while the predicted trajectory considers both posterior and prior knowledge without increasing the computation complexity. The experimental results show that the proposed trajectory prediction model can adapt to different driving scenarios and predict trajectories with high accuracy due to the unique combination of the prior and posterior knowledge in the driving scene.

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