Intelligent Signal Processing System Design

智能信号处理系统被定义为设计和构建的智能系统，使应用程序能够在变化的环境中感知分析相关信号，感知和解释信号中的相关信息，适应和学习处理和预测信号，模仿，自动化和增强人类的一些智能行为，以满足最终用户的需求。构建智能信号处理系统需要开发的核心技术如下。递归联想记忆是一种深度递归神经网络，可以学习和回忆二值模式和灰度图像。该网络允许两种记忆召回模式，即通过输入层和输出层的模式对进行召回，以及通过输入层或输出层的单一模式进行召回。在本部分的研究中，将开发循环联想记忆应使用用于不同类型信号的特征提取和/或降噪，并开发快速预处理和有效学习算法，以实现高效和有效的操作。实离散周期序列的离散Gabor变换被定义为该序列与移位分析（或高斯）窗口乘积的离散傅里叶变换，以产生用于时频分析的复离散Gabor系数。在这一部分的研究中，将为离散Gabor变换及其多窗口版本开发新的理论、方法和算法，对信号（或图像）进行动态时频分析，以有效地提取特征。一个深度s形处理系统由多个块非线性模型组成，这些模型可以作为知识记忆的全局模型进行离线训练来建模和处理或预测信号。全局模型与存储局部信号知识记忆的块非线性模型构建的在线自适应局部模型协同工作。从相同输入获得的全局模型和局部模型的组合输出旨在更好地处理或预测输入信号。该系统的设计将避免灾难性遗忘，并使向后迁移学习成为可能。在这一部分的研究中，将开发方法和算法来设计和构建针对日常应用的低复杂度的深度s型处理系统。在深度s型处理系统的基础上，用模糊神经元代替其全局和局部模型中各块非线性模型的非线性，构造了一个深度模糊神经系统。该系统将被设计用于推理和解释由深度模糊神经系统输入得出的结论。在这部分研究中，将开发方法和算法来设计和构建深度模糊神经系统，目标是需要类似人类推理的更复杂和复杂的应用。

An intelligent signal processing system is defined as an intelligent system designed and built to enable applications that can sense to analyze relevant signals in a changing environment, perceive and interpret to model the relevant information from the signals, adapt and learn to process and predict the signals, to imitate, automate, and augment some intelligent behaviors of human beings to serve the needs of the end users. The core technologies to be developed to build intelligent signal processing systems are described below. Recurrent associative memory is a deep recurrent neural network that can learn and recall binary patterns and gray level images. The network allows two modes of memory recall, namely, recalling by a pattern-pair from both the input and the output layers, and recalling by a single-pattern from either the input layer or the output layer. In this part of the research, recurrent associative memory shall be used for feature extraction and/or noise reduction of different types of signals, and fast preprocessing and effective learning algorithms will be developed for efficient and effective operations. Discrete Gabor transform of a real discrete periodic sequence is defined as the discrete Fourier transform of the product of the sequence and a shifted analysis (or Gaussian) window to yield the complex discrete Gabor coefficients for time-frequency analysis. In this part of the research, new theories, methods, and algorithms will be developed for discrete Gabor transform and its multiwindow versions to perform dynamic time-frequency analysis of signals (or images) to effectively extract features. A deep sigmoid processing system consists of a combination of block nonlinear models which can be offline trained to model and process or predict signals as a global model of knowledge memory. The global model is designed to work collaboratively with an online adaptive local model constructed by a block nonlinear model which stores the knowledge memory of the local signals. The combined output of the global and local models obtained from the same input is designed to better process or predict an input signal. The system will be designed to avoid catastrophic forgetting and enable backward transfer learning. In this part of the research, methods and algorithms will be developed to design and build deep sigmoid processing systems with low complexity targeted to day-to-day applications. A deep fuzzy neural system is constructed from a deep sigmoid processing system by replacing the nonlinearity of each block nonlinear model in its global and local models by a fuzzy neuron. The system will be designed to reason and explain conclusions reached from inputs by the deep fuzzy neural system. In this part of the research, methods and algorithms will be developed to design and build deep fuzzy neural systems targeted to more sophisticated and complex applications that require human-like reasoning.