Kotaro Hirasawa

A Double-Deck Elevator Group Supervisory Control System Using Genetic Network Programming

Elevator group supervisory control systems (EGSCSs) are designed so that the movement of several elevators in a building is controlled efficiently. The efficient control of EGSCSs using conventional control methods is very difficult due to its complexity, so it is becoming popular to introduce artificial intelligence (AI) technologies into EGSCSs in recent years. As a new approach, a graph-based evolutionary method named genetic network programming (GNP) has been applied to the EGSCSs, and its effectiveness is clarified. The GNP can introduce various a priori knowledge of the EGSCSs in its node functions easily, and can execute an efficient rule-based group supervisory control that is optimized in an evolutionary way. Meanwhile, double-deck elevator systems (DDESs) where two cages are connected in a shaft have been developed for the rising demand of more efficient transport of passengers in high-rise buildings. The DDESs have specific features due to the connection of cages and the need for comfortable riding; so its group supervisory control becomes more complex and requires more efficient group control systems than the conventional single-deck elevator systems (SDESs). In this paper, a new group supervisory control system for DDESs using GNP is proposed, and its optimization and performance evaluation are done through simulations. First, optimization of the GNP for DDSEs is executed. Second, the performance of the proposed method is evaluated by comparison with conventional methods, and the obtained control rules in GNP are studied. Finally, the reduction of space requirements compared with SDESs is confirmed.

Comparison between Genetic Network Programming (GNP) and Genetic Programming (GP)

Recently, many methods of evolutionary computation such as genetic algorithm (GA) and genetic programming (GP) have been developed as a basic tool for modeling and optimizing of complex systems. Generally speaking, GA has the genome of a string structure, while the genome in GP is the tree structure. Therefore, GP is suitable for constructing complicated programs, which can be applied to many real world problems. However, GP might sometimes be difficult to search for a solution because of its bloat. A novel evolutionary method named Genetic Network Programming (GNP), whose genome is a network structure is proposed to overcome the low searching efficiency of GP and is applied to the problem of the evolution of ant behavior in order to study the effectiveness of GNP. In addition, the comparison of the performances between GNP and GP is carried out in simulations on ant behaviors.

An Intrusion-Detection Model Based on Fuzzy Class-Association-Rule Mining Using Genetic Network Programming

As the Internet services spread all over the world, many kinds and a large number of security threats are increasing. Therefore, intrusion detection systems, which can effectively detect intrusion accesses, have attracted attention. This paper describes a novel fuzzy class-association-rule mining method based on genetic network programming (GNP) for detecting network intrusions. GNP is an evolutionary optimization technique, which uses directed graph structures instead of strings in genetic algorithm or trees in genetic programming, which leads to enhancing the representation ability with compact programs derived from the reusability of nodes in a graph structure. By combining fuzzy set theory with GNP, the proposed method can deal with the mixed database that contains both discrete and continuous attributes and also extract many important class-association rules that contribute to enhancing detection ability. Therefore, the proposed method can be flexibly applied to both misuse and anomaly detection in network-intrusion-detection problems. Experimental results with KDD99Cup and DARPA98 databases from MIT Lincoln Laboratory show that the proposed method provides competitively high detection rates compared with other machine-learning techniques and GNP with crisp data mining.

Universal learning network and its application to chaos control

Universal Learning Networks (ULNs) are proposed and their application to chaos control is discussed. ULNs provide a generalized framework to model and control complex systems. They consist of a number of inter-connected nodes where the nodes may have any continuously differentiable nonlinear functions in them and each pair of nodes can be connected by multiple branches with arbitrary time delays. Therefore, physical systems, which can be described by differential or difference equations and also their controllers, can be modeled in a unified way, and so ULNs may form a super set of neural networks and fuzzy neural networks. In order to optimize the ULNs, a generalized learning algorithm is derived, in which both the first order derivatives (gradients) and the higher order derivatives are incorporated. The derivatives are calculated by using forward or backward propagation schemes. These algorithms for calculating the derivatives are extended versions of Back Propagation Through Time (BPTT) and Real Time Recurrent Learning (RTRL) of Williams in the sense that generalized node functions, generalized network connections with multi-branch of arbitrary time delays, generalized criterion functions and higher order derivatives can be deal with. As an application of ULNs, a chaos control method using maximum Lyapunov exponent of ULNs is proposed. Maximum Lyapunov exponent of ULNs can be formulated by using higher order derivatives of ULNs, and the parameters of ULNs can be adjusted so that the maximum Lyapunov exponent approaches the target value. From the simulation results, it has been shown that a fully connected ULN with three nodes is able to display chaotic behaviors.

Class Association Rule Mining with Chi-Squared Test Using Genetic Network Programming

An efficient algorithm for important class association rule mining using genetic network programming (GNP) is proposed. GNP is one of the evolutionary optimization techniques, which uses directed graph structures as genes. Instead of generating a large number of candidate rules, the method can obtain a sufficient number of important association rules for classification. The proposed method measures the significance of the association via the chi-squared test. Therefore, all the extracted important rules can be used for classification directly. In addition, the method suits class association rule mining from dense databases, where many frequently occurring items are found in each tuple. Users can define conditions of extracting important class association rules. In this paper, we describe an algorithm for class association rule mining with chi-squared test using GNP and present a classifier using these extracted rules.

A quasi-ARMAX approach to modelling of non-linear systems

This paper proposes a class of quasi-ARMAX models for non-linear systems. Similar to ordinary non-linear ARMAX models, the quasi-ARMAX models are flexible black-box models, but they have various linearity properties similar to those of linear ARMAX models. A modelling scheme is introduced to construct models consisting of two parts: a macro-part and a kernel-part. By using Taylor expansion and other mathematical transformation techniques, it is first constructed as a class of quasi-ARMAX interfaces (macro-parts) that have various linearity properties but contain some complicated coefficients. MIMO neurofuzzy models (kernel-parts) are then introduced to represent the complicated coefficients. It is shown that the proposed quasi-ARMAX models have both good approximation ability and some easy-to-use properties. The proposed models have been successfully applied to prediction, fault detection and adaptive control of non-linear systems.

A genetic network programming with learning approach for enhanced stock trading model

In this paper, an enhancement of stock trading model using Genetic Network Programming (GNP) with Sarsa Learning is described. There are three important points in this paper: First, we use GNP with Sarsa Learning as the basic algorithm while both Technical Indices and Candlestick Charts are introduced for efficient stock trading decision-making. In order to create more efficient judgment functions to judge the current stock price appropriately, Importance Index (IMX) has been proposed to tell GNP the timing of buying and selling stocks. Second, to improve the performance of the proposed GNP-Sarsa algorithm, we proposed a new method that can learn the appropriate function describing the relation between the value of each technical index and the value of the IMX. This is an important point that devotes to the enhancement of the GNP-Sarsa algorithm. The third point is that in order to create more efficient judgment functions, sub-nodes are introduced in each node to select appropriate stock price information depending on the situations and to determine appropriate actions (buying/selling). To confirm the effectiveness of the proposed method, we carried out the simulation and compared the results of GNP-Sarsa with other methods like GNP with Actor Critic, GNP with Candlestick Chart, GA and Buy&Hold method. The results shows that the stock trading model using GNP-Sarsa outperforms all the other methods.

The dynamic performance of photovoltaic supplied dc motor fed from DC-DC converter and controlled by neural networks

This paper presents an adaptive neural network controller (ANNC) that is used to control the speed of a separately excited DC motor driving a centrifugal pump load and fed from photovoltaic (PV) generator through DC-DC buck-boost converter. The controller is also used to track the maximum power point (MPP) of the PV generator by controlling the converter duty ratio. Such kinds of controllers must have two objective functions to perform these two tasks, but in this research the objective function related to the MPP is converted to a constrained for the second objective function by making some approximation in the system equations. An adaptive neural network identifier (ANNI), which emulates the dynamic behavior of the motor system, plays an important role in computing the system Jacobian and hence updating the weights and biases of the ANNC. The weights and biases of both networks are updated on line using a BP algorithm with adaptive learning rate. The computation of the adaptive learning rate is based on the value of the speed error through an empirical formula to get faster response with less oscillation and minimum overshoot. The transient response of the motor speed, current and voltage for a step change in the reference speed and the insolation are presented.

Online learning of genetic network programming (GNP)

A new evolutionary computation method called genetic network programming (GNP) was proposed recently. In this paper, an online learning method for GNP is proposed. This method uses Q learning to improve its state transition rules so that it can make GNP adapt to dynamic environments efficiently.

Learning Petri network and its application to nonlinear system control

According to recent knowledge of brain science it is suggested that there exists functions distribution, which means that specific parts exist in the brain for realizing specific functions. This paper introduces a new brain-like model called Learning Petri Network (LPN) that has the capability of functions distribution and learning. The idea is to use Petri net to realize the functions distribution and to incorporate the learning and representing ability of neural network into the Petri net. The obtained LPN can be used in the same way as a neural network to model and control dynamic systems, while it is distinctive to a neural network in that it has the capability of functions distribution. An application of the LPN to nonlinear crane control systems is discussed. It is shown via numerical simulations that the proposed LPN controller has superior performance to the commonly-used neural network one.