In a probabilistic graph (network), source-to-all-terminal (SAT) reliability may be defined as the probability that there exists at least one path consisting only of successful arcs from source vertex s to every other vertex. In this paper, we define an optimal SAT reliability formula to be the one with minimal number of literals or operators. At first, this paper describes an arc-reductions (open- or short-circuiting) method for obtaining a factored formula of directed graph. Next, we discuss a simple strategy to get an optimal formula being a product of the reliability formulas of vertex-section graphs, each of which contains a distinct strongly connected component of the given graph. This method reduces the computing cost and data processing effort required tu generate the optimal factored formula, which contains no identical product terms.

Let U denote a set comprising elements called "keys." The goal of the nearest point problem is to search quickly for a key among some keys x1 , xn that is the nearest to a reference key x under a partial order relation defined as (x, y) (x, z) for x, y, zU if d(x, y)d(x, z) given a wide-sense distance measure d. This article proposes a method of rearranging x1 , xn into a binary perfectly-balanced tree for solving quickly the nearest point problems. Further, computational performances of the proposed method are evaluated experimentally.

The self-organizing feature map is one of the most widely used neural network paradigm based on unsupervised competitive learning. However, the learning algorithm introduced by Kohonen is very slow when the size of the map is large. The slowness is caused by the search for large map in each training steps of the learning. In this paper, a fast learning algorithm based on incremental ordering is proposed. The new learning starts with only a few units evenly distributed on a large topological feature map, and gradually increases the number of units until it covers the entire map. In middle phases of the learning, some units are well-ordered and others are not, while all units are weekly-ordered in Kohonen learning. The ordered units, during the learning, help to accelerate the search speed of the algorithm and accelerate the movements of the remaining unordered units to their topological locations. It is shown by theoretical analysis as well as experimental analysis that the proposed learning algorithm reduces the training time from O(M2) to O(log M) for M by M map without any additional working space, while preserving the ordering properties of the Kohonen learning algorithm.

Recently, fuzzy logic which is a kind of infinite multiple-valued logic has been studied to treat certain ambiguities, and its algebraic properties have been studied by the name of fuzzy logic functions. In order to treat modality (necessity, possibility) in fuzzy logic, which is an important concept of multiple-valued logic, the intuitionistic logical negation is required in addition to operations of fuzzy logic. Infinite multiple-valued logic functions introducing the intuitionistic logical negation into fuzzy logic functions are called Kleene-Stone logic functions, and they enable us to treat modality. The domain of modality in which Kleene-Stone logic functions can handle, however, is too limited. We will define α-KS logic functions as infinite multiple-valued logic functions using a unary operation instead of the intuitionistic logical negation of Kleene-Stone logic functions. In α-KS logic functions, modality is closer to our feelings. In this paper we will show some algebraic properties of α-KS logic functions. In particular we prove that any n-variable α-KS logic function is determined uniquely by all inputs of 7 values which are 7 specific truth values of the original infinite truth values. This means that there is a bijection between the set of α-KS logic functions and the set of 7-valued α-KS logic functions which are restriction of α-KS logic functions to 7 specific truth values. Finally, we show a necessary and sufficient condition for a 7-valued logic function to be a 7-valued α-KS logic function.

In this paper, we will define Kleene-Stone logic functions which are functions F: [0, 1]n[0, 1] including the intuitionistic negation into fuzzy logic functions, and they can easily represent the concepts of necessity and possibility which are important concepts of many-valued logic systems. A set of Kleene-Stone logic functions is one of the models of Kleene-Stone algebra, which is both Kleene algebra and Stone algebra, as same as a set of fuzzy logic functions is one of the models of Kleene algebra. This paper, especially, describes some algebraic properties and representation of Kleene-Stone logic functions.

Kleene-Stone algebra is both Kleene algebra and Stone algebra. The set of Kleene-Stone logic functions discussed in this paper is one of the models of Kleene-Stone algebra, and they can easily represent the concepts of necessity and possibility which are important concepts for many-valued logic systems. Main results of this paper are that the followings are clarified: a necessary and sufficient condition for a function to be a Kleene-Stone logic function and a formula representing the number of n-variable Kleene-Stone logic functions.

This paper describes the higher-order moment analysis of superposed Markov jumping processes. A superposed Markov jumping process is defined as a linear superposition of a finite number of piecewise constant real valued stochastic process whose value changes are associated with state transitions in an underlying descrete state continuous time Markov process. Some phenomena are modeled well by the process such as membrane current fluctuations observed at bio-membranes or load fluctuations in electrical power systems. Theoretical formula of the moment function of any order k is derived and the parameter estimation problem utilizing higher-order moment functions is discussed. A new method of estimating the kinetic parameters of membrane current fluctuations is proposed as a possible application.

The purpose of our research is to get further improvement in the performance of order statistic filters. The basic idea found in our research is the use of a robust median estimator to obtain median differential order information which the classes of order statistic filter required in order to sort the input signal in the filter window. In order to give the motivation for using a median estimator in the classes of order statistic filters, we derive theorems characterizing the median filters and prove them theoretically using the characteristic that the order statistic filter has the performance for a monotonic signal equivalent with the FIR linear filter. As an application of median operation, we propose and investigate the Median Differential Order Statistic Filter to reduce impulsive noise as well as Gaussian noise and regard it as a subclass of the Order Statistic Filter. Moreover, we introduce the piecewise linear function in the Median Differential Order Statistic Filter to improve performance in terms of edge preservation. We call it the Piecewise Linear Median Differential Order Statistic Filter. The effectiveness of proposed filters is verified theoretically by computing the output Mean Square Error of the filters in parts of edge signals, impulsive noise, small amplitude noise and their combination. Computer simulations also show that the proposed filter can improve the performance in both noise (small-amplitude Gaussian noise and impulsive noise) reduction and edge preservation for one-dimensional signals.

Binary Decision Diagrams (BDDs) and Shared Binary Decision Diagrams (SBDDs), which are improved BDDs, are useful for implementing VLSI logic design systems. Recently, these representations, which are graph representations of Boolean functions, have become popular because of their efficiency in terms of time and space. The forms of the BDD vary with the order of the input variables though they represent the same function. The size of the graphs greatly depends on the order. The variable ordering algorithm is one of the most important issues in the application of BDDs. In this paper, we consider methods which reduce the graph size by reordering input variables on a given BDD with a certain variable order. We propose the Minimum-Width Method which gives a considerably good order in a practicable time and space. In the method, the order is determined by width of BDDs as a cost function. In addition, we show the effect of combining our method with the local search method, and also describe the improvement using the threshold. Experimental results show that our method can reduce the size of BDDs remarkably for most examples. The method needs no additional information, such as the topological information of the circuit. The results can be a measure for evaluation of other ordering methods.