Aihara Electrical Engineering Co.

Funabashi, Japan

Aihara Electrical Engineering Co.

Funabashi, Japan

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Hirata Y.,University of Tokyo | Yamada T.,Aihara Electrical Engineering Co. | Takahashi J.,Aihara Electrical Engineering Co. | Suzuki H.,University of Tokyo
Physics Letters, Section A: General, Atomic and Solid State Physics | Year: 2012

An online multi-step prediction method is constructed based on data streams, for which using all observed data points for prediction is impractical. The proposed method is superior in prediction accuracy and computational time, at various prediction steps, compared to two simple extensions of Kwasniok and Smith [F. Kwasniok, L.A. Smith, Phys. Rev. Let. 92 (2004) 164101]. We apply our proposed prediction method to artificial data sets generated from the Lorenz-63 model and to measured wind speed data streams. © 2012 Elsevier B.V.


Shimada Y.,Saitama University | Yamada T.,AIHARA Electrical Engineering Co. | Ikeguchi T.,Saitama University
International Journal of Bifurcation and Chaos | Year: 2012

We propose a simple but effective visualization method for detecting the stretch-and-fold mechanism in chaotic dynamical systems. In the proposed method, we first place a hypersphere that is centered at a point on the trajectory of an attractor produced from the chaotic dynamical system. Second, we uniformly arrange points on the surface of the hypersphere. Third, we evolve the dynamics and observe the temporal evolution of the center point and the points on the surface. We then calculate the temporal evolution of the distances between the center point and the points on the surface. Finally, we express them using multiple colors and draw the colors representing the distances at the initial position on the hypersphere. Then, we observe the temporal changes of these colors generated by the temporal evolution of the distances. Application of the proposed method to chaotic dynamics results in the appearance of stripe patterns on the surface of the hypersphere over time. The results indicate that we can detect the stretch-and-fold mechanism, or the horse-shoe structure embedded in chaotic dynamics. We also discussed the reason for the generation of the stripe pattern under the framework of the proposed method. © 2012 World Scientific Publishing Company.


Munakata T.,Cleveland State University | Takahashi J.,Aihara Electrical Engineering Co. | Sekikawa M.,Tohoku University | Aihara K.,University of Tokyo
International Journal of Parallel, Emergent and Distributed Systems | Year: 2010

Chaos computing is a non-traditional new paradigm that exploits the extreme non-linearity of chaotic systems. This article presents a unified theoretical view of chaos computing. It introduces the fundamental concept and the unique features that are characteristics of chaos computing, and discusses various implementation approaches. Basic aspects of digital chaos computing to realise logical gates are introduced, followed by two specific techniques: (1) direct utilisation of the threshold mechanisms; (2) an application of the chaos neuron model. After presenting these approaches, we discuss general characteristics of digital chaos computing. Other digital, analog and digital/analog hybrid forms of chaos computing are also considered. Potential advantages of chaos computing include: high speed, low power and low cost, a general-purpose form of computing, re-configurable or dynamic logical architecture, implementation of continuous logic, robustness against noise, and parallel and distributed computing. © 2010 Taylor & Francis.


Hirata Y.,University of Tokyo | Hirata Y.,Japan Science and Technology Agency | Yamada T.,AIHARA Electrical Engineering Co. | Takahashi J.,AIHARA Electrical Engineering Co. | And 3 more authors.
Renewable Energy | Year: 2014

We propose a general method for predicting multiple steps ahead of our target system and estimating simultaneously the prediction errors in a real time. The requirement of the proposed method is that we have a time series of the target system. We demonstrate the method by artificial data, real wind speed data, and real solar irradiation data. © 2013 The Authors.

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