Fully Tuned Radial Basis Function Neural Networks for Flight Control

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Step 2. Initialize the momentum factor and learning rate parameter. Step 3. When the stopping condition is false, perform Steps 4— Step 4. For each training dataset vector pair do Steps 5— Phase 2 feed forward phase. Phase 3 error radial basis function phase. Phase 4 updating of weights and bias. Pseudocode 2. Figure 4. Simulation Results for the Proposed Non-Cost-Sensitive Prediction Model The costs of false positive rate and false negative rate are not considered in this section during the training process. Table 7 Performance results of the proposed predictor on the five NASA datasets non-cost-sensitive case.

Figure 5. Simulation Results for the Proposed Cost-Sensitive Prediction Model The main focus made in this paper is the development of cost-sensitive radial basis function classifier model to classify the software entities that are defect-free or defect-prone. Table 9 Performance and comparison results of the proposed predictor on the five NASA datasets cost-sensitive case, four different cost ratios.

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Figure 6. Competing Interests The authors declare that they have no competing interests. References 1. An empirical study on software defect prediction with a simplified metric set. Information and Software Technology. Wang T. Multiple kernel ensemble learning for software defect prediction.


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Automated Software Engineering. Yang X. A learning-to-rank approach to software defect prediction.

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Arora I. Open issues in software defect prediction. Procedia Computer Science. Software defect prediction using cost-sensitive neural network.


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Applied Soft Computing Journal. Yang Z. A novel radial basis function neural network for discriminant analysis. Ghosh-Dastidar S. Principal component analysis-enhanced cosine radial basis function neural network for robust epilepsy and seizure detection. Lian R.

Adaptive self-organizing fuzzy sliding-mode radial basis-function neural-network controller for robotic systems.


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    The adaptive radial basis function neural network for small rotary-wing unmanned aircraft. Chapman M. Metrics data program. Abaei G. An empirical study based on semi-supervised hybrid self-organizing map for software fault prediction. Knowledge-Based Systems. Vandecruys O. Mining software repositories for comprehensible software fault prediction models. Journal of Systems and Software. Kumar R. A radial basis function neural network classifier for the discrimination of individual odor using responses of thick-film tin-oxide sensors. Yeung D.

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    Xie N. Blind equalization using a predictive radial basis function neural network. Jafarnejadsani H.

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    Meng K. Self-adaptive radial basis function neural network for short-term electricity price forecasting.

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    Gao X. Kalman filtering compensated by radial basis function neural network for seam tracking of laser welding. Chang G. Radial-basis-function-based neural network for harmonic detection. Yingwei L. Dash P.

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    Application of minimal radial basis function neural network to distance protection. Wong T. Low-angle radar tracking using radial basis function neural network. Khairnar D. Radial basis function neural network for pulse radar detection. Jain T. Fast voltage contingency screening using radial basis function neural network. Wong Y. Radial basis function neural network with incremental learning for face recognition. Platt J. Advances in Neural Information Processing Systems. A constructive RBF network for writer adaptation. Using analytic QP and sparseness to speed training of support vector machines.