Intro
Preface
Organization
Contents - Part I
Contents - Part II
Neural Network (NN) Theory, NN-Based Control Systems, Neuro-System Integration and Engineering Applications
ESN-Based Control of Bending Pneumatic Muscle with Asymmetric and Rate-Dependent Hysteresis
1 Introduction
2 The FSBPM
2.1 Structure of the FSBPM
2.2 The Asymmetric Rate-Dependent Hysteresis of the FSBPM
2.3 Mathematical Description of the FSBPM
3 Feedback Control Strategy Combined with the Feedforward Compensation
3.1 Inversion of the Hysteresis Based on ESN

3.2 Feedback Control Strategy Based on Feedforward Compensation
4 Experiments
4.1 Experimental Platform
4.2 Feedback Control Experiments
5 Conclusions
References
Image Reconstruction and Recognition of Optical Flow Based on Local Feature Extraction Mechanism of Visual Cortex
1 Introduction
2 Methods
2.1 MT Optical Flow Stimulation
2.2 Image Reconstruction Using NMF Algorithm
2.3 Image Reconstruction Using the SNN Model
3 Results
4 Conclusion
4.1 Summary
4.2 Outlook
References

Conditional Diffusion Model-Based Data Augmentation for Alzheimer's Prediction
1 Introduction
2 Method
2.1 Overview
2.2 Diffusion Probabilistic Model
2.3 Conditional DDPM
3 Experiments
3.1 Dataset and Experiment Design
3.2 Evaluation of Generated Data
3.3 Evaluation with Compared Methods
4 Conclusion
References
Design of Dissolved Oxygen Online Controller Based on Adaptive Dynamic Programming Theory
1 Introduction
2 Preliminary Knowledge
2.1 Optimal Problem Formulation
2.2 Online ESN-ADP Algorithm
2.3 FRRLS Algorithm for Training ESN-ADP

3 Convergence of ESN-Based Value Function Approximation
3.1 Convergence of ESN-Based Value Function Approximation
4 Experiment and Discussion
5 Conclusion
References
Ascent Guidance for Airbreathing Hypersonic Vehicle Based on Deep Neural Network and Pseudo-spectral Method
1 Introduction
2 Dynamic Modeling
3 Guidance Law Design
3.1 Offline Trajectory Database Establishment
3.2 DNN Structure and Training
3.3 The Sequential Calling Strategy and the Overall Scheme
4 Numerical Simulations
4.1 The Database Establishment
4.2 Real-Time Performance

4.3 Comparison of Different Numbers of the DNNs
5 Conclusion
References
Machine Learning and Deep Learning for Data Mining and Data-Driven Applications
Image Intelligence-Assisted Time-Series Analysis Method for Identifying "Dispersed, Disordered, and Polluting" Sites Based on Power Consumption Data
1 Introduction
2 Preliminary
2.1 Clustering Algorithm
2.2 Gramian Angular Field
3 Algorithm Procedure
3.1 H-K-means
3.2 Imaging Time Series
3.3 Mutual Information
3.4 Perceptual Hash Algorithm
4 Practical Validation
4.1 Background
4.2 Implementation