Advanced digital filters without and with neural techniques

滤波器是通信、消费电子和其他领域不断扩大的产品频谱中的核心子系统。新兴应用对滤波器的稳健性、速度和功率提出了越来越高的要求，以便在不断缩短的产品周期内完成。本研究试图为相关问题提供切实可行的解决方案。滤波器的鲁棒性是指它的系数灵敏度、舍入噪声和稳定性，以及它处理溢出的能力。不同的滤波器结构具有不同的鲁棒性。对于特定的应用程序，要实现所需的健壮性，最优结构是什么？实时功能和应用程序要求滤波器以极快的速度和低延迟运行。不同的滤波器结构具有不同的速度和延迟特性。对于特定的应用程序，实时操作的最佳结构是什么？如今，便携式应用对电池的要求越来越高，工作时间也越来越长，而过热会降低处理速度和寿命。有不同的技术来降低功耗。对于特定的应用，低功耗解决方案的最佳策略是什么？一旦确定了滤波器结构，我们需要为应用设计其系数。递归滤波器更为紧凑，但其设计涉及到没有全局解的非线性问题。对于一个特定的应用程序，什么是最好的设计策略？为了回答上述问题，我们建议继续研究数字滤波器、Gabor滤波器和自适应神经/模糊神经滤波器的最佳结构和设计算法。这里总结了一些研究思路。在寻找最优滤波器结构时，滤波器结构的鲁棒性与其无源性和稳定性之间的关系是什么？滤波器中最耗时、最耗能的元件是乘法器。有没有一种聪明的方法来减少或消除过滤器结构中的乘数？如果可行，它确实提供了一个高速和低功耗的解决方案。解决递归滤波器非线性设计难题的最佳方法是什么？如何结合技术，如结合全局和本地搜索？

Filters are core sub-systems in an expanding spectrum of products in communications, consumer electronics, and others. Emerging applications have placed an increasing demand on the robustness, speed, and power of a filter to be accomplished within a shrinking product cycle. This research attempts to provide practical solutions to the associated problems. The robustness of a filter refers to its coefficient sensitivity, roundoff noise, and stability properties, and to its abilities to handle overflow. Different filter structures have different robustness properties. For a specific application, what will be the optimal structure to achieve desired robustness? Real-time functionalities and applications demand a filter to operate extremely fast and with low delay. Different filter structures have difference speed and delay properties. For a specific application, what is the best structure for real-time operations? Nowadays, portable applications impose an increasing demand on battery with a longer operating time, while overheating reduces processing speed and life. There exist different techniques for reducing the power consumption. For a specific application, what will be the best strategy for low-power solution? Once a filter structure is decided, we need to design its coefficients for applications. Recursive filters are more compact but their designs involve nonlinear problems that have no global solution. For a specific application, what will be the best design strategy? To answer the above questions, we propose to continue research on optimal structures and design algorithms for digital filters, Gabor filters, and adaptive neural/fuzzy-neural filters. Some of the research ideas are summarized here. In the search of an optimal filter structure, what is the relationship between the robustness of a filter structure and its passivity and stability properties? The most time-consuming and power-consuming element in a filter is multiplier. Is there a smart way to reduce or eliminate multipliers in a filter structure? If feasible, it would indeed provide a high-speed and low-power solution. What is the best way to solve the difficult nonlinear design problem of a recursive filter? How about a combination of techniques such as a combined global and local search?