Wei Chih-Hsiu, Fahn Chin-Shyurng
Nat. Taiwan Univ. of Sci. and Technol., Taipei.
IEEE Trans Neural Netw. 2002;13(3):600-18. doi: 10.1109/TNN.2002.1000127.
In this paper, a new neural architecture, the multisynapse neural network, is developed for constrained optimization problems, whose objective functions may include high-order, logarithmic, and sinusoidal forms, etc., unlike the traditional Hopfield networks which can only handle quadratic form optimization. Meanwhile, based on the application of this new architecture, a fuzzy bidirectional associative clustering network (FBACN), which is composed of two layers of recurrent networks, is proposed for fuzzy-partition clustering according to the objective-functional method. It is well known that fuzzy c-means is a milestone algorithm in the area of fuzzy c-partition clustering. All of the following objective-functional-based fuzzy c-partition algorithms incorporate the formulas of fuzzy c-means as the prime mover in their algorithms. However, when an application of fuzzy c-partition has sophisticated constraints, the necessity of analytical solutions in a single iteration step becomes a fatal issue of the existing algorithms. The largest advantage of FBACN is that it does not need analytical solutions. For the problems on which some prior information is known, we bring a combination of part crisp and part fuzzy clustering in the third optimization problem.
本文针对约束优化问题开发了一种新的神经架构——多突触神经网络，其目标函数可能包括高阶、对数和正弦形式等，这与传统的霍普菲尔德网络不同，后者只能处理二次形式的优化。同时，基于这种新架构的应用，提出了一种由两层递归网络组成的模糊双向联想聚类网络（FBACN），用于根据目标函数法进行模糊划分聚类。众所周知，模糊c均值是模糊c划分聚类领域的一个里程碑算法。以下所有基于目标函数的模糊c划分算法都将模糊c均值的公式作为其算法的主要推动力。然而，当模糊c划分的应用具有复杂约束时，在单个迭代步骤中解析解的必要性成为现有算法的一个致命问题。FBACN的最大优势在于它不需要解析解。对于一些已知先验信息的问题，我们在第三个优化问题中引入了部分清晰和部分模糊聚类的组合。
