• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.39 no.1
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• pp.16-24
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• 2011

In this paper, we propose a new guidance law for target observability enhancement, which can control both terminal impact angle and acceleration. The proposed guidance law is simple form, combined conventional time-to-go polynomial guidance and a additional bias term which consists of relative position and proportional gain. The guidance law provides oscillatory flight trajectory and it maintains the conventional time-to-go polynomial guidance performance. To investigate the characteristics of the guidance law, we derive the closed-form solution, and various simulations are performed for proving the validity of the proposed guidance.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.2
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• pp.312-320
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• 2012

In this study, we introduce a new design methodology of a granular-oriented self-organizing polynomial neural networks (GoSOPNNs) that is based on multi-layer perceptron with Context-based Polynomial Neurons (CPNs) or Polynomial Neurons (PNs). In contrast to the typical architectures encountered in polynomial neural networks (PNN), our main objective is to develop a methodological design strategy of GoSOPNNs as follows : (a) The 1st layer of the proposed network consists of Context-based Polynomial Neuron (CPN). In here, CPN is fully reflective of the structure encountered in numeric data which are granulated with the aid of Context-based Fuzzy C-Means (C-FCM) clustering method. The context-based clustering supporting the design of information granules is completed in the space of the input data while the build of the clusters is guided by a collection of some predefined fuzzy sets (so-called contexts) defined in the output space. (b) The proposed design procedure being applied at each layer of GoSOPNN leads to the selection of preferred nodes of the network (CPNs or PNs) whose local characteristics (such as the number of contexts, the number of clusters, a collection of the specific subset of input variables, and the order of the polynomial) can be easily adjusted. These options contribute to the flexibility as well as simplicity and compactness of the resulting architecture of the network. For the evaluation of performance of the proposed GoSOPNN network, we describe a detailed characteristic of the proposed model using a well-known learning machine data(Automobile Miles Per Gallon Data, Boston Housing Data, Medical Image System Data).

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.38 no.7
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• pp.664-672
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• 2010

This paper provides the target observability analysis of time-to-go polynomial guidance law with bearing-only measurement. In this study, a direct approach is used to analyze the target observability. Since the observability condition of a constant-velocity target is given by the function of LOS angle only, the target observability characteristic is determined by substituting the closed form solution of LOS angle to the observability condition directly. The analysis results show that the target observability is depended on the choice of guidance gain, initial intercept condition and guidance command shape. After that this mathematical analysis result is evaluated and demonstrated by number of simulation.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.38 no.8
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• pp.774-780
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• 2010

This paper deals with the choice of guidance gain for the time-to-go polynomial (POLY) guidance law when the acceleration limit is existed. POLY is derived based on the assumption that guidance commands are formed by a time-to-go polynomial function. The main characteristic of POLY is that any positive values can be used for its guidance gain. For this reason, it is ambiguous to choose a proper guidance gain. To relieve this difficulty, we firstly derive the closed-form solution of acceleration command and figure out the relationship between the maximum acceleration and guidance gain. From this analysis, we provide a guideline for choosing a guidance gain which satisfies the desired acceleration limit. Finally, the proposed method is demonstrated by simulation study.

• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2633-2635
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• 2003

In this study, we propose a fuzzy polynomial neural networks (FPNN) and a genetically optimized fuzzy polynomial neural networks(GoFPNN) for identification of non-linear system. GoFPNN architecture is designed by a FPNN based on fuzzy set and its structure and parameters are optimized by genetic algorithms. A fuzzy neural networks(FNN) based on fuzzy set divide into two structures that is simplified inference structure and linear inference structure. The proposed FPNN is resulted from integration and extension of simplified and linear inference structure of FNN. The consequence structure of the FPNN consist of polynomials represented by networks using connection weights for rules. The networks comprehend simplified(Type 0), linear (Type 1), and quadratic(Type 3) inferences. The proposed FPNN can select polynomial type of consequence part for each rule. Therefore, proposed scheme can offer flexible structure design capability for a system characteristics. Moreover, GAs is applied to networks structure and parameters tuning of proposed FPNN, and its efficient application method is discussed, these subjects are result in GoFPNN that is optimal FPNN. To evaluate proposed model performance, a numerical experiment is carried out.

• The Transactions of the Korea Information Processing Society
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• v.6 no.9
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• pp.2431-2441
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• 1999

Equations are widely used in representing information. One of the basic questions about equations is to determine whether a given equation follows logically from the set of equations. Rewrite systems are one of the method to answer many instances of this problem. A rewrite system simplifies a given term by applying rewrite rules successively. Hence it is important that the process of simplification does not go on indefinitely. One of the methods to check whether a rewrite system terminates (that is, the rewrite system does not go on indefinitely) is polynomial orderings. A polynomial ordering assigns an appropriate polynomial to each function symbol. However, how to assign polynomials to function symbols is not known. We propose an automatic way of generating polynomial orderings using genetic algorithms.

• International Journal of Aeronautical and Space Sciences
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• v.8 no.1
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• pp.46-53
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• 2007

In this paper, a guidance law for vision-based automatic landing of unmanned aerial vehicles (UAVs) is proposed. Automatic landing is a challenging but crucial capability for UAVs to achieve a fully autonomous flight. In an autonomous landing maneuver of UAVs, the decision of where to landing and the generation of guidance command to achieve a successful landing are very significant problem. This paper is focused on the design of guidance law applicable to automatic landing problem of fixed-wing UAV and rotary-wing UAV, simultaneously. The proposed guidance law generates acceleration command as a control input which derived from a specified time-to-go ($t_go$) polynomial function. The coefficient of $t_go$-polynomial function are determined to satisfy some terminal constraints. Nonlinear simulation results using a fixed-wing and rotary-wing UAV models are presented.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.46 no.10
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• pp.814-822
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• 2018

This paper proposes a guidance law for missiles with control time constraint. Because the proposed guidance law is based on a time-to-go polynomial, it has a generalized form. Also, acceleration of the proposed law converges to zero at the end of the control time, which reduces the sensitivity to the time-to-go estimation error and can increase the flight stability when the separation of the missile appears. A prediction method of the time-to-go is proposed for implementing the proposed law, and the possibility of application to the midcourse and terminal guidance phases is dealt with for exoatmospheric interception. The characteristics and performance of the proposed law are analyzed throughout various simulations.

• Journal of the Korean Mathematical Society
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• v.53 no.2
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• pp.415-431
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• 2016

This note is concerned with examining nilradicals and Jacobson radicals of polynomial rings when related factor rings are Armendariz. Especially we elaborate upon a well-known structural property of Armendariz rings, bringing into focus the Armendariz property of factor rings by Jacobson radicals. We show that J(R[x]) = J(R)[x] if and only if J(R) is nil when a given ring R is Armendariz, where J(A) means the Jacobson radical of a ring A. A ring will be called feckly Armendariz if the factor ring by the Jacobson radical is an Armendariz ring. It is shown that the polynomial ring over an Armendariz ring is feckly Armendariz, in spite of Armendariz rings being not feckly Armendariz in general. It is also shown that the feckly Armendariz property does not go up to polynomial rings.

• Bulletin of the Korean Mathematical Society
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• v.50 no.6
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• pp.1957-1972
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• 2013

The reflexive property for ideals was introduced by Mason and has important roles in noncommutative ring theory. In this note we study the structure of idempotents satisfying the reflexive property and introduce reflexive-idempotents-property (simply, RIP) as a generalization. It is proved that the RIP can go up to polynomial rings, power series rings, and Dorroh extensions. The structure of non-Abelian RIP rings of minimal order (with or without identity) is completely investigated.