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Recurrent neural networks (RNNs) have good modeling capability for nonlinear dynamic systems, but due to the difficulties for training this superiority is discounted. Echo state network (ESN) is a new paradigm for using RNNs with a simpler training method, where an RNN is generated randomly and only a readout is trained. ESN method has quickly become popular in robotics, such as for motor control,...
Autonomous mobile robots must accomplish tasks in unknown and noisy environments. In this context, learning robot behaviors in an imitation based approach would be desirable in the perspective of service robotics as well as of learning robots. In this work, we use reservoir computing (RC) for learning robot behaviors by demonstration. In RC, a randomly generated recurrent neural network, the reservoir,...
Cerebellar Model Articulation Controller (CMAC) NN is a computational model of cerebellum introduced as an alternative to backpropagated multilayer networks to control robot arms. From then it has seen many improvements and has been applied in many other areas as a general NN. These improvements have been in the context of generalization, learning techniques, differentiability, memory size, fuzzification...
We proposed a compressed sensing Super Resolution algorithm based on wavelet. The proposed algorithm performs well with a smaller quantity of training image patches and outputs images with satisfactory subjective quality. It is tested on classical images commonly adopted by Super Resolution researchers with both generic and specialized training sets for comparison with other popular commercial software...
Autonomous robotic excavation has often been limited to a single robotic platform using a specified excavation vehicle. This paper presents a novel method for developing scalable controllers for use in multirobot scenarios and that do not require human defined operations scripts nor extensive modeling of the kinematics and dynamics of the excavation vehicles. Furthermore, the control system does not...
Autonomous robotic systems and intelligent artificial agents' capability have advanced dramatically. Since the intelligent artificial agents have been developing more autonomous and human-like, the capability of them to make moral decisions becomes an important issue. In this work we developed an artificial neutral network which considered various effective factors for ethical assessment of an action...
Physical activity (PA) is commonly recognized to directly influence changes in heart rate (HR). HR prediction based on PA can be a useful tool in medical research and monitoring in a clinical setting. In our previous works, predictors with high accuracy were designed. However, the HR could only be predicted in single time steps. In this study, a multi-step HR prediction method is proposed. Firstly,...
The gear box fault occur can lead to the fatal breakdown of mechanical system. Back propagation neural network (BPNN) have been proved to be of widespread utility for identifying and classifying gear box faults to prevent serious damage in a mechanical system. Some researchers have used particle swarm optimization (PSO) to train BPNN. However, because the PSO algorithm has several parameters to be...
Extreme learning machine (ELM) is one of the effective training algorithms for single hidden layer feedforward neural networks (SLFNs), but it often requires a large number of hidden units which makes the trained networks respond slowly to input patterns. Regularized least-squares extreme learning machine (RLS-ELM) is one of the improvements which can overcome this problem. It determines the input...
Improving Fuzzy Logic System (FLS) design is of main interest. Linguistic rules of a FLS can be converted into Fuzzy Basis Functions (FBFs). Moreover, numerical rules and their FBFs can be extracted from numerical training data. This combination of both linguistic and numerical information simultaneously makes the FBFs very useful. Since a specific FLS can be expressed as a linear combination of FBFs,...
This paper describes a method for improving the generalization performance of bagging ensemble by means of using Bayesian approach. We examine the Bayesian prediction using bagging leaning machines for regression problems, and show a method to reduce the generalization loss defined by the square error of the prediction for test data. We examine and validate the effectiveness via numerical experiments...
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