Aircraft Parameter Estimation using Feedforward Neural Networks With Lyapunov Stability Analysis

Aerodynamic parameter estimation is critical in the aviation sector, especially in design and development programs of defense-military aircraft. In this paper, new results of the application of Artificial Neural Networks (ANN) to the field of aircraft parameter estimation are presented. The performances of Feedforward Neural Network (FFNN) with Backpropagation and FFNN with Backpropagation using Recursive Least Square (RLS) are investigated for aerodynamic parameter estimation. The methods are validated on flight data simulated using MATLAB implementations. The normalized Lyapunov energy functional has been used to derive the convergence conditions for both the ANN-based estimation algorithms. The estimation results are compared on the basis of performance metrics and computation time. The performance of FFNN-RLS has been observed to be approximately 10% better than FFNN-BPN. Simulation results from both algorithms have been found to be highly satisfactory and pave the way for further applications to real flight test data

Development of Advanced Verification and Validation Procedures and Tools for the Certification of Learning Systems in Aerospace Applications

Adaptive control technologies that incorporate learning algorithms have been proposed to enable automatic flight control and vehicle recovery, autonomous flight, and to maintain vehicle performance in the face of unknown, changing, or poorly defined operating environments. In order for adaptive control systems to be used in safety-critical aerospace applications, they must be proven to be highly safe and reliable. Rigorous methods for adaptive software verification and validation must be developed to ensure that control system software failures will not occur. Of central importance in this regard is the need to establish reliable methods that guarantee convergent learning, rapid convergence (learning) rate, and algorithm stability. This paper presents the major problems of adaptive control systems that use learning to improve performance. The paper then presents the major procedures and tools presently developed or currently being developed to enable the verification, validation, and ultimate certification of these adaptive control systems. These technologies include the application of automated program analysis methods, techniques to improve the learning process, analytical methods to verify stability, methods to automatically synthesize code, simulation and test methods, and tools to provide on-line software assurance

A Review on Tomato Leaf Disease Detection using Deep Learning Approaches

Agriculture is one of the major sectors that influence the India economy due to the huge population and ever-growing food demand. Identification of diseases that affect the low yield in food crops plays a major role to improve the yield of a crop. India holds the world's second-largest share of tomato production. Unfortunately, tomato plants are vulnerable to various diseases due to factors such as climate change, heavy rainfall, soil conditions, pesticides, and animals. A significant number of studies have examined the potential of deep learning techniques to combat the leaf disease in tomatoes in the last decade. However, despite the range of applications, several gaps within tomato leaf disease detection are yet to be addressed to support the tomato leaf disease diagnosis. Thus, there is a need to create an information base of existing approaches and identify the challenges and opportunities to help advance the development of tools that address the needs of tomato farmers. The review is focussed on providing a detailed assessment and considerations for developing deep learning-based Convolutional Neural Networks (CNNs) architectures like Dense Net, ResNet, VGG Net, Google Net, Alex Net, and LeNet that are applied to detect the disease in tomato leaves to identify 10 classes of diseases affecting tomato plant leaves, with distinct trained disease datasets. The performance of architecture studies using the data from plantvillage dataset, which includes healthy and diseased classes, with the assistance of several different architectural designs. This paper helps to address the existing research gaps by guiding further development and application of tools to support tomato leaves disease diagnosis and provide disease management support to farmers in improving the crop

On the training of feedforward neural networks.

by Hau-san Wong.Thesis (M.Phil.)--Chinese University of Hong Kong, 1993.Includes bibliographical references (leaves [178-183]).Chapter 1 --- INTRODUCTIONChapter 1.1 --- Learning versus Explicit Programming --- p.1-1Chapter 1.2 --- Artificial Neural Networks --- p.1-2Chapter 1.3 --- Learning in ANN --- p.1-3Chapter 1.4 --- Problems of Learning in BP Networks --- p.1-5Chapter 1.5 --- Dynamic Node Architecture for BP Networks --- p.1-7Chapter 1.6 --- Incremental Learning --- p.1-10Chapter 1.7 --- Research Objective and Thesis Organization --- p.1-11Chapter 2 --- THE FEEDFORWARD MULTILAYER NEURAL NETWORKChapter 2.1 --- The Perceptron --- p.2-1Chapter 2.2 --- The Generalization of the Perceptron --- p.2-4Chapter 2.3 --- The Multilayer Feedforward Network --- p.2-5Chapter 3 --- SOLUTIONS TO THE BP LEARNING PROBLEMChapter 3.1 --- Introduction --- p.3-1Chapter 3.2 --- Attempts in the Establishment of a Viable Hidden Representation Model --- p.3-5Chapter 3.3 --- Dynamic Node Creation Algorithms --- p.3-9Chapter 3.4 --- Concluding Remarks --- p.3-15Chapter 4 --- THE GROWTH ALGORITHM FOR NEURAL NETWORKSChapter 4.1 --- Introduction --- p.4-2Chapter 4.2 --- The Radial Basis Function --- p.4-6Chapter 4.3 --- The Additional Input Node and the Modified Nonlinearity --- p.4-9Chapter 4.4 --- The Initialization of the New Hidden Node --- p.4-11Chapter 4.5 --- Initialization of the First Node --- p.4-15Chapter 4.6 --- Practical Considerations for the Growth Algorithm --- p.4-18Chapter 4.7 --- The Convergence Proof for the Growth Algorithm --- p.4-20Chapter 4.8 --- The Flow of the Growth Algorithm --- p.4-21Chapter 4.9 --- Experimental Results and Performance Analysis --- p.4-21Chapter 4.10 --- Concluding Remarks --- p.4-33Chapter 5 --- KNOWLEDGE REPRESENTATION IN NEURAL NETWORKSChapter 5.1 --- An Alternative Perspective to Knowledge Representation in Neural Network: The Temporal Vector (T-Vector) Approach --- p.5-1Chapter 5.2 --- Prior Research Works in the T-Vector Approach --- p.5-2Chapter 5.3 --- Formulation of the T-Vector Approach --- p.5-3Chapter 5.4 --- Relation of the Hidden T-Vectors to the Output T-Vectors --- p.5-6Chapter 5.5 --- Relation of the Hidden T-Vectors to the Input T-Vectors --- p.5-10Chapter 5.6 --- An Inspiration for a New Training Algorithm from the Current Model --- p.5-12Chapter 6 --- THE DETERMINISTIC TRAINING ALGORITHM FOR NEURAL NETWORKSChapter 6.1 --- Introduction --- p.6-1Chapter 6.2 --- The Linear Independency Requirement for the Hidden T-Vectors --- p.6-3Chapter 6.3 --- Inspiration of the Current Work from the Barmann T-Vector Model --- p.6-5Chapter 6.4 --- General Framework of Dynamic Node Creation Algorithm --- p.6-10Chapter 6.5 --- The Deterministic Initialization Scheme for the New Hidden NodesChapter 6.5.1 --- Introduction --- p.6-12Chapter 6.5.2 --- Determination of the Target T-VectorChapter 6.5.2.1 --- Introduction --- p.6-15Chapter 6.5.2.2 --- Modelling of the Target Vector βQhQ --- p.6-16Chapter 6.5.2.3 --- Near-Linearity Condition for the Sigmoid Function --- p.6-18Chapter 6.5.3 --- Preparation for the BP Fine-Tuning Process --- p.6-24Chapter 6.5.4 --- Determination of the Target Hidden T-Vector --- p.6-28Chapter 6.5.5 --- Determination of the Hidden Weights --- p.6-29Chapter 6.5.6 --- Determination of the Output Weights --- p.6-30Chapter 6.6 --- Linear Independency Assurance for the New Hidden T-Vector --- p.6-30Chapter 6.7 --- Extension to the Multi-Output Case --- p.6-32Chapter 6.8 --- Convergence Proof for the Deterministic Algorithm --- p.6-35Chapter 6.9 --- The Flow of the Deterministic Dynamic Node Creation Algorithm --- p.6-36Chapter 6.10 --- Experimental Results and Performance Analysis --- p.6-36Chapter 6.11 --- Concluding Remarks --- p.6-50Chapter 7 --- THE GENERALIZATION MEASURE MONITORING SCHEMEChapter 7.1 --- The Problem of Generalization for Neural Networks --- p.7-1Chapter 7.2 --- Prior Attempts in Solving the Generalization Problem --- p.7-2Chapter 7.3 --- The Generalization Measure --- p.7-4Chapter 7.4 --- The Adoption of the Generalization Measure to the Deterministic Algorithm --- p.7-5Chapter 7.5 --- Monitoring of the Generalization Measure --- p.7-6Chapter 7.6 --- Correspondence between the Generalization Measure and the Generalization Capability of the Network --- p.7-8Chapter 7.7 --- Experimental Results and Performance Analysis --- p.7-12Chapter 7.8 --- Concluding Remarks --- p.7-16Chapter 8 --- THE ESTIMATION OF THE INITIAL HIDDEN LAYER SIZEChapter 8.1 --- The Need for an Initial Hidden Layer Size Estimation --- p.8-1Chapter 8.2 --- The Initial Hidden Layer Estimation Scheme --- p.8-2Chapter 8.3 --- The Extension of the Estimation Procedure to the Multi-Output Network --- p.8-6Chapter 8.4 --- Experimental Results and Performance Analysis --- p.8-6Chapter 8.5 --- Concluding Remarks --- p.8-16Chapter 9 --- CONCLUSIONChapter 9.1 --- Contributions --- p.9-1Chapter 9.2 --- Suggestions for Further Research --- p.9-3REFERENCES --- p.R-1APPENDIX --- p.A-