Radial Basis Function Networks

Du, Ke-Lin; Swamy, M. N. S.

doi:10.1007/978-1-4471-5571-3_10

Cited by 36 publications

(18 citation statements)

References 106 publications

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“…Further, the activation function of the RBF models is the Gaussian radial basis functions and there are d nodes (neurons) in the hidden layer, where d is the dimension of the decision space. The centers of the Gaussian functions of the RBF models are specified using the k-means clustering algorithm, the widths are set to be the maximum distance between the centers, and the weights from the hidden nodes to the output node are determined using the pseudo-inverse method [73].…”

Section: Experimental Results On Benchmark Problemsmentioning

confidence: 99%

Offline Data-Driven Evolutionary Optimization Using Selective Surrogate Ensembles

Wang

Jin

Sun

et al. 2019

IEEE Trans. Evol. Computat.

175

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In solving many real-world optimization problems, neither mathematical functions nor numerical simulations are available for evaluating the quality of candidate solutions. Instead, surrogate models must be built based on historical data to approximate the objective functions and no new data will be available during the optimization process. Such problems are known as offline data-driven optimization problems. Since the surrogate models solely depend on the given historical data, the optimization algorithm is able to search only in a very limited decision space during offline data-driven optimization. This paper proposes a new offline data-driven evolutionary algorithm to make the full use of the offline data to guide the search. To this end, a surrogate management strategy based on ensemble learning techniques developed in machine learning is adopted, which builds a large number of surrogate models before optimization and adaptively selects a small yet diverse subset of them during the optimization to achieve the best local approximation accuracy and reduce the computational complexity. Our experimental results on the benchmark problems and a transonic airfoil design example show that the proposed algorithm is able to handle offline data-driven optimization problems with up to 100 decision variables.

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Section: Experimental Results On Benchmark Problemsmentioning

confidence: 99%

Offline Data-Driven Evolutionary Optimization Using Selective Surrogate Ensembles

Wang

Jin

Sun

et al. 2019

IEEE Trans. Evol. Computat.

175

View full text Add to dashboard Cite

show abstract

“…Our version of DIRECT is similar to that described in Guenther and Micci Barreca ( 1997 ) as we also use a hyperplane radial basis function (RBF) network (Stokbro et al, 1990 ; Du and Swamy, 2014 ) as our choice of neural network. However, we do not attempt to learn the inverse map, but instead only learn the forward map and then use it to numerically approximate the instantaneous Jacobian matrix.…”

Section: Methodsmentioning

confidence: 99%

A neural network-based exploratory learning and motor planning system for co-robots

2015

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Collaborative robots, or co-robots, are semi-autonomous robotic agents designed to work alongside humans in shared workspaces. To be effective, co-robots require the ability to respond and adapt to dynamic scenarios encountered in natural environments. One way to achieve this is through exploratory learning, or “learning by doing,” an unsupervised method in which co-robots are able to build an internal model for motor planning and coordination based on real-time sensory inputs. In this paper, we present an adaptive neural network-based system for co-robot control that employs exploratory learning to achieve the coordinated motor planning needed to navigate toward, reach for, and grasp distant objects. To validate this system we used the 11-degrees-of-freedom RoPro Calliope mobile robot. Through motor babbling of its wheels and arm, the Calliope learned how to relate visual and proprioceptive information to achieve hand-eye-body coordination. By continually evaluating sensory inputs and externally provided goal directives, the Calliope was then able to autonomously select the appropriate wheel and joint velocities needed to perform its assigned task, such as following a moving target or retrieving an indicated object.

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“…In the classification case, the output of the model learning of the RBF Neural Network for the input pattern x is as follow (Du and Swamy, 2014):…”

Section: Rbf Neural Networkmentioning

confidence: 99%

Landslide Hazard Mapping Using a Radial Basis Function Neural Network Model: A Case Study in Semirom, Isfahan, Iran

Yavari

Pahlavani

Bigdeli

2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. In this paper, Radial Basis Function (RBF) Neural Network and Logistic Regression (LR) models were proposed for hazard prediction of landslides in a part of the Semirom area (Iran) to compare their accuracy and performance. For this purpose, a spatial database of the study area was prepared that consists of 68 landslide locations and 11 influencing information layers including slope, aspect, profile curvature, plan curvature, distance from faults, distance from roads, distance from residential regions, distance from rivers, land use, lithology and rainfall. Landslide hazard maps were prepared for the study area by applying the proposed algorithms. Performance of the models was assessed using the Receiver Operating Characteristic (ROC) curve and area under the ROC curve (AUC). The coefficient of determination (R2), the root mean square error (RMSE), and the Normal Root Mean Square Error (NRMSE) were calculated for proposed methods. The outcomes showed that the RBF Neural Network has the highest R2 (0.8224), in comparison to that of the LR model (0.5365). Also, the ROC plots, RMSEs and NRMSEs showed that the proposed RBF Neural Network is much better than the LR model. Consequently, it can be concluded that the RBF Neural Network is the best regression model in this study and it can be considered as a capable method for landslide hazard mapping in landslide-susceptible areas.

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Radial Basis Function Networks

Cited by 36 publications

References 106 publications

Offline Data-Driven Evolutionary Optimization Using Selective Surrogate Ensembles

Offline Data-Driven Evolutionary Optimization Using Selective Surrogate Ensembles

A neural network-based exploratory learning and motor planning system for co-robots

Landslide Hazard Mapping Using a Radial Basis Function Neural Network Model: A Case Study in Semirom, Isfahan, Iran

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