Levenberg-Marquardt Backpropagation Algorithm Neural Network을 이용한 디젤엔진 동정과 속도제어에 관한 연구

Diesel engine is known as nonlinear system because of its dead time due to injection delay and ignition delay. So, it is very difficult and complex to model this nonlinear system because it varies widely according to number of cylinder and RPM. In this paper, in order to design the speed control system of a diesel engine, neural network architecture is introduced and the optimal structure of neuro emulator is determined based on the modelling of a diesel engine, trained with various backpropagation algorithms and the performance of each trained networks is compared . Also, neuro controller, the inversely trained neural network of neuro emulator, is designed for the speed control system of a diesel engine. The selective neuro controller is proposed for the sake of improvement of the neuro controller performance and by combining a PI controller with the proposed controller, the efficiency of this combination speed control system of a diesel engine is ascertained.？疇？ Chapter 1. Introduction = 5 1.1 Background = 5 1.2 Study Objective = 8 Chapter 2. Review of Neural Networks = 10 2.1 Neuron Model = 10 2.2 Neural Networks = 14 2.3 Learning of Neural Networks = 15 2.3.1 Simple Backpropagation = 16 2.3.2 Backpropagation with Momentum(BPM)18 2.3.3 Adaptive Backpropagation(BPA) = 18 2.3.4 Fast Backpropagation(BPX) = 19 2.3.5 Levenberg-Marquardt Backpropagation(BPLM) = 19 2.4 Initialization of Neural Networks = 20 Chapter 3. Design of Neuro Emulator for Diesel Engine 22 3.1 Modelling of a Diesel Engine System = 22 3.2 Structure of a Neuro Emulator = 24 3.3 Data Collection Method = 25 3.4 Training Results and Analysis with respect to Various Backpropagation Algorithms = 29 Chapter 4. Design of a Neuro Controller for Diesel Engine = 33 4.1 Neuro Controller Design = 33 4.2 Design of a Neuro Control System = 36 4.3 Design of Combination Control System with PI and Neuro Controller = 39 Chapter 5. Conclusion = 42 Reference = 4

Modeling Financial Time Series with Artificial Neural Networks

Financial time series convey the decisions and actions of a population of human actors over time. Econometric and regressive models have been developed in the past decades for analyzing these time series. More recently, biologically inspired artificial neural network models have been shown to overcome some of the main challenges of traditional techniques by better exploiting the non-linear, non-stationary, and oscillatory nature of noisy, chaotic human interactions. This review paper explores the options, benefits, and weaknesses of the various forms of artificial neural networks as compared with regression techniques in the field of financial time series analysis.CELEST, a National Science Foundation Science of Learning Center (SBE-0354378); SyNAPSE program of the Defense Advanced Research Project Agency (HR001109-03-0001

Feedback control by online learning an inverse model

A model, predictor, or error estimator is often used by a feedback controller to control a plant. Creating such a model is difficult when the plant exhibits nonlinear behavior. In this paper, a novel online learning control framework is proposed that does not require explicit knowledge about the plant. This framework uses two learning modules, one for creating an inverse model, and the other for actually controlling the plant. Except for their inputs, they are identical. The inverse model learns by the exploration performed by the not yet fully trained controller, while the actual controller is based on the currently learned model. The proposed framework allows fast online learning of an accurate controller. The controller can be applied on a broad range of tasks with different dynamic characteristics. We validate this claim by applying our control framework on several control tasks: 1) the heating tank problem (slow nonlinear dynamics); 2) flight pitch control (slow linear dynamics); and 3) the balancing problem of a double inverted pendulum (fast linear and nonlinear dynamics). The results of these experiments show that fast learning and accurate control can be achieved. Furthermore, a comparison is made with some classical control approaches, and observations concerning convergence and stability are made

Complex partial synchronization patterns in networks of delay-coupled neurons

We study the spatio-temporal dynamics of a multiplex network of delay-coupled FitzHugh–Nagumo oscillators with non-local and fractal connectivities. Apart from chimera states, a new regime of coexistence of slow and fast oscillations is found. An analytical explanation for the emergence of such coexisting partial synchronization patterns is given. Furthermore, we propose a control scheme for the number of fast and slow neurons in each layer.DFG, 163436311, SFB 910: Kontrolle selbstorganisierender nichtlinearer Systeme: Theoretische Methoden und Anwendungskonzept

Photonic Delay Systems as Machine Learning Implementations

Nonlinear photonic delay systems present interesting implementation platforms for machine learning models. They can be extremely fast, offer great degrees of parallelism and potentially consume far less power than digital processors. So far they have been successfully employed for signal processing using the Reservoir Computing paradigm. In this paper we show that their range of applicability can be greatly extended if we use gradient descent with backpropagation through time on a model of the system to optimize the input encoding of such systems. We perform physical experiments that demonstrate that the obtained input encodings work well in reality, and we show that optimized systems perform significantly better than the common Reservoir Computing approach. The results presented here demonstrate that common gradient descent techniques from machine learning may well be applicable on physical neuro-inspired analog computers