Adaptive Integral Sliding Mode Control Using Fully Connected Recurrent Neural Network for Position and Attitude Control of Quadrotor.

This article proposes an adaptive integral sliding mode control (ISMC) strategy for quadrotor control that ensures faster and finite-time convergence along with chattering attenuation. Quadrotor dynamics are assumed to be unknown because of the high degree of parametric uncertainties, including external disturbances. The equivalent control law obtained by ISMC consists of quadrotor dynamics and, thus, cannot be applied to the quadrotor. A new fully connected recurrent neural network (FCRNN) controller has been proposed to mimic the equivalent control instead of estimating the Quadrotor dynamics separately. The proposed FCRNN architecture consists of output feedback to the input layer and the hidden layer, which enhances the approximation capability of FCRNN. All hidden layer neurons receive self-feedback and feedback from other hidden layer neurons, which further strengthens FCRNN's potential to capture complex dynamic characteristics. As learning should happen in finite time, the finite-time stability of the overall system has been guaranteed using the Lyapunov stability theory, and the update laws for FCRNN weights in real time are derived using the same. To show the effectiveness of the proposed approach, a comprehensive analysis has been done against existing SMC strategy and against well-known function approximation techniques, e.g., the radial basis function network (RBFN) and RNN.

[The clinical value of combining multiple features of CT in discriminating between benign and malignant lung lesions].

OBJECTIVE: To develop a set of combined criteria of multiple features of chest CT for discriminating between benign and malignant lung lesions. METHODS: Patients whose chest CT showed abnormalities were recruited from the West China Hospital in March and April 2010. The patients were examined with bronchoscopy and the results of CT and pathology were compared. RESULTS: A total of 105 patients participated in this study and 85 had confirmed pathological results. The CT identified 27 cases of malignant lesions, 22 of which were confirmed by the pathology. The CT identified 58 cases of benign lesions, 55 of which were confirmed by the pathology. The set of combined criteria of multiple features of chest CT had an accuracy of 90.59%, a sensitivity of 88.00%, and a specificity of 91.67% in diagnosing benign and malignant lung lesions. CONCLUSION: The combined criteria of multiple imaging signs of CT have good clinical values for diagnosing malignant lung lesions.