Research On Time Domain Parameter Measurement Based On Neural Network And Chaos And Application In Digital Oscilloscope

To solve the problems that have existed in the detection applications of chaos modeland neural network model,the feasibility of detecting technology based on chaos theoryis elaborated in terms of theory in this dissertation.The principles of chaos detectionmodel,neural network detection model and the composite detection model of theaforementioned are further studied.The time-domain parameters to detect the signals inthe chaotic background by adopting the detecting technology in the appliance of thechaos theory and neural network theory are presented.This study demonstrates positiveapplications by reversing the existing principles.By reversing the chaos detectionprinciple,a new method to extract signal parameters more effectively by building thechaos model and the neural network model to detect the weak-signal time-domainparameters in the chaotic state directly,has been explored.Simultaneously,because ofthe dynamic and non-linear characteristics of time-domain detection system,and thedifficulties in updating the mathematical model,a neural network inverse system hasbeen built to obtain the nonlinear dynamic characteristics of original system by usingthe inverse system theory.In essence,the modeling algorithm,the model's structure andmodeling methods are studied in depth in order to widen the application scope of usingchaos and neural network theory in the time-domain detection and the circuit dynamicparameters to improve the detection accuracy.The research results are applied in digital oscilloscope,the typical time-domaindetection equipment.To solve the problems of incapability in detecting weak signalswith the digital oscilloscope for its poor performance in capturing weak trigger signalsand invalidation of updating the static and dynamic parameters with the classic theories,this dissertation are focused on enhancing the capability of DSO weak signal detectionand weak trigger signal capture.Calibration models based on chaos theory and inversesystem model based on neural network are constructed in this dissertation.Innovativestatic,steady-state and dynamic calibration methods are also recreated.In this research,the author has contributed in the following major areas: â… .Analysis and research on the neural network to identify and detect signals.Thealgorithms of space division competition neural networks to identify the regularanalog signal classifications are studied.The opinion which identifies the signaltype with the digital oscilloscope by competition neural network provides a basisfor the selection of interpolation algorithm.â…¡.Research on structure,stability and applied of Elman space-time network.Thestructure and learning methods of Elman time-space network,especially thevalidity of its application to detect time-domain signals are discussed.Thealgorithm and stability of improved Elman space-time network are studied and itsadvantages are also proved by emulation.A new time-domain detection model hasbeen constructed on the basis of chaos theory and neural network.â…¢.Research on the weak periodic signal detection in the chaotic background.Chaoticdetecting model is constructed by Duffing-Holmes equation to detect weakperiodic signal frequency;and the applications of two-dimension Henon map indetecting weak trigger signals and digital oscilloscope time-base are discussed.â…£.Research on weak transient signals detecting based on chaos and neural networktechnology.The structure of front-faced network and design method based on FPalgorithm are studied.A detection model to detect weak transient signals directlyin the chaotic background is constructed.â…¤.Research on the weak signal time-domain parameters detection based on chaosand neural network technology.The detection model and method of chaoticsystem and neural network to obtain time-domain signal parameters directly in thechaotic state are researched.The structure of space-time neural network isfurther-studied,based on which the weak time-domain signal detection model byusing the chaotic neural network has been constructed.â…¥.Research on the kick-out pulse in DSO calibration and"NTN"correction methods.The accuracy,time-based error estimation and the rise-time detection andcalibration methods in the application of digital oscilloscope voltage are studied aswell as chaos calibration model of static parameters is constructed.New ideas toadopt the neural network inverse system to solve the DSO dynamic parameterscalibration are proposed.The"NTN"updating methods and kick-out pulse are studied to provide theoretical basis for the broadband high-speed digitaloscilloscope calibration.