An adaptive threshold learning algorithm for classical conditioning

Clouse, R.L.; Kim, Soo-Won; Waldron, Manjula B.

doi:10.1109/iembs.1997.756635

Cited by 4 publications

(3 citation statements)

References 3 publications

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“…Recently reinforcement learning was applied in robot behavior learning has become one of the focal point of research. The theory foundation of reinforcement learning in fact is both the classical conditioning proposed by Pavlov who is physiologist of Russia [2] and operant conditioning(OC) proposed by B F Skinner who is American Harvard psychologist Professor [3]. RL is suitable for simple task; in contrast Skinner OC can suitable for various task and so operant conditioning will have will have more wide application.…”

Section: Introductionmentioning

confidence: 99%

Self-Balance Control of Inverted Pendulum Based on Fuzzy Skinner Operant Conditioning

Cai

Ruan

2009

2009 International Conference on Information Technology and Computer Science

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This paper constructs an operant conditioning learning system based on fuzzy and probabilistic automata, which used for on-line self-learning of fuzzy rules. The learning system can learn its rules on line by interaction with environment, and achieve the best rule consequent. The probability cans grantee the global superiority of learning mechanism. The fuzzy inference can improve the robustness and rapidity and of learning. Furthermore, we adopt two fuzzy control structure in order to avoid rule explosion problem that mean the rules will increase in exponent which induced by the more number of input variable, which can predigest difficulty of design. We apply our model to inverted pendulum selfbalance control and the simulation indicate: the designed operant conditioning learning system can realize the selflearning of fuzzy rules, and it especially has outstanding superiority for dealing with lack of prior knowledge.

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Section: Introductionmentioning

confidence: 99%

Self-Balance Control of Inverted Pendulum Based on Fuzzy Skinner Operant Conditioning

Cai

Ruan

2009

2009 International Conference on Information Technology and Computer Science

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“…McFarland D and Bosser T presented in [5] that the bionic design method is superior to traditional way, which make the control system possess abundant characteristic. The classical conditioning which is proposed by Russia physiologist Pavlov [6] and operant conditioning(OC) which is proposed by America Harvard university psychologist professor B F Skinner o [7,8] are the two kinds of important conditioning reflex in history of psychology. The Pavlov conditioning is regarded as the fundamental learning form and the Skinner OC is more emphasized on the results of behavior influencing on behavior compared with pre.…”

Section: Introductionmentioning

confidence: 99%

Self-organization stochastic fuzzy control based on OCPFA and applied on self-balanced robot

Cai

Ruan

2010

2010 8th World Congress on Intelligent Control and Automation

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This paper constructs a stochastic fuzzy controller to realize self-balancing control of two-wheeled robot. The rule of fuzzy controller is stochastic, which is automatically generated by an OCPFA learning system and optimized online. The OCPFA learning system is in fact a Probabilistic Finite Automata (PFA) which based on Skinner Operant Conditioning (Skinner OC), and it is composed by a bionic reorientation mechanism and an OC learning mechanism. The reorientation mechanism take for orientation function of robot posture balance as the goal-orientation, and response to the output control variable of fuzzy stochastic controller; Learning mechanism updated probability contribution of output control variable by using the response information form the environment and then choose the optimal control variable according to the new probability contribution. The OCPFA learning system gradually adapts to the environment changes by interacting with the dynamic environment based on which the learning system realize the self-organization of stochastic fuzzy control rules and show character of autonomous learning. In the same time the theoretical prove that the process of autonomous learning is convergent in sense of probability. The simulation indicate the stochastic fuzzy controller can successfully applied in twowheeled robot self-balancing without requiring the model of the robot and the robot can show control behavior of autonomous learning which similar to animal's OC learning behavior.

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“…However, the process of continuously updating multicharacteristic parameters often resulted in auditing delays because the value was too large, which is unsuitable for online fault detection. Clouse et al 12 adopted the method of neural networks to train both the threshold and the weight, which made the threshold more adaptive. This method has good adaptability and nonlinearity, and it can reduce the rate of false positives and false alarms in fault detection.…”

Section: Introductionmentioning

confidence: 99%

An adaptive threshold algorithm for sensor fault based on the grey theory

Yao

Peng

et al. 2017

Advances in Mechanical Engineering

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An appropriate threshold is the key factor in a diagnosis of fault. However, the traditional method of setting a fixed threshold does not take into consideration the influence of system status and noise interference, and it often leads to false alarms and missed detections of system fault. To improve the accuracy of fault diagnosis, we first obtained the residual signal based on the strong tracking filter method -cubature Kalman filtering. We then proposed an adaptive dynamic threshold adjustment algorithm based on the grey theory. In this method, the threshold value can be dynamically adjusted according to the real-time mean and variance of the residual. Finally, we performed a sensor fault experiment involving three sensors in different locations of a robot. The results demonstrate the feasibility of our proposed method.

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An adaptive threshold learning algorithm for classical conditioning

Cited by 4 publications

References 3 publications

Self-Balance Control of Inverted Pendulum Based on Fuzzy Skinner Operant Conditioning

Self-Balance Control of Inverted Pendulum Based on Fuzzy Skinner Operant Conditioning

Self-organization stochastic fuzzy control based on OCPFA and applied on self-balanced robot

An adaptive threshold algorithm for sensor fault based on the grey theory

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