Learning and evolution are two fundamental forms of adaptation. There has been a great interest in combining learning and evolution with artificial neural networks (ANNs) in recent years. This paper: 1) reviews different combinations between ANNs and evolutionary algorithms (EAs), including using EAs to evolve ANN connection weights, architectures, learning rules, and input features; 2) discusses different search operators which have been used in various EAs; and 3) points out possible future research directions. It is shown, through a considerably large literature review, that combinations between ANNs and EAs can lead to significantly better intelligent systems than relying on ANNs or EAs alone.

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Evolving artificial neural networks

In this paper we review classification algorithms used to design brain–computer interface (BCI) systems based on electroencephalography (EEG). We briefly present the commonly employed algorithms and describe their critical properties. Based on the literature, we compare them in terms of performance and provide guidelines to choose the suitable classification algorithm(s) for a specific BCI.

/pdf/a-review-of-classification-algorithms-for-eeg-based-brain-p1h22hb1wr.pdf

A review of classification algorithms for EEG-based brain–computer interfaces

Personal Communications

While the study of psychology has offered little in the way of explaining the creative process, Koestler examines the idea that we are at our most creative when rational thought is suspended--for example, in dreams and trancelike states. All who read The Act of Creation will find it a compelling and illuminating book.

The Act of Creation

人教版高中英语新课程教材中,语言运用（Using Language）是每个单元必不可少的部分,提供了围绕单元中心话题的听、说、读、写的综合性练习,是单元中心话题的延续和升华.如何设计Using Language部分的教学,使自己的教学模式既不落俗套,又能真正体现新课程标准所倡导的教学理念,正是广大一线英语教师一直努力探索的问题.

打磨Using Language,倡导新理念

It is proposed that electromyographic (EMG) patterns can be analyzed and classified by neural networks for EMG-controlled prosthetic members. The thrust of this work is that neural networks make EMG pattern recognition much easier and more efficient; thus, use of EMG control in a prosthetic arm/hand would involve much less physical and mental effort on the part of the subject. The FFT-analyzed pattern for a one-channel surface EMG of the flexor digitorium superficialis can be recognized by the standard back-propagation neural network. Classification of the five finger movements based on the recognition of their patterns is successfully accomplished. >

EMG pattern analysis and classification by neural network

Electroencephalography (EEG) pattern-recognition studies were carried out using EEG topography (readiness potential, or RP, spatiotemporal patterns) generated the moment before voluntary movements of muscles. RPs generated prior to pronouncing syllables and controlling a joystick were studied by experiments and simulation. The spatiotemporal patterns of RPs were measured by multichannel surface electrodes pasted on the subject's scalp. Backpropagation neural networks were used for RP pattern recognition. The results show that RPs generated prior to syllable pronouncement contain some information about those syllables, and that RPs generated prior to joy stick movements contain information on the direction of intended movement. They also show that neural networks can be used to recognize EEG information and so create a new type of man-machine interface for data input. >

EEG topography recognition by neural networks

A new system is proposed towards the computational frame-work of evolutionary hardware that adaptively changes its structure and behavior according to the environment In the proposed system, hardware specifications, which produce hardware structures and behaviors, are automatically generated as Hardware Description Language (HDL) programs Using a rewriting system, the system introduces a program development process, that imitates the natural development process from pollinated egg to adult and gives the HDL-program flexible evolvability Also discussed is a method to evolve the language itself by modifying the corresponding rewriting system This method is intended to serve as hierarchal mechanism of evolution and to contribute to the evolvability of large-scale hardware Although this paper's discussion is mainly involves in HDL-programs because our goal is hardware evolution, the techniques described here are applicable to ordinary computer programs written in such conventional formats as “C” language

Development and Evolution of Hardware Behaviors

A new numerical analysis is proposed to investigate accurately the propagation characteristics of a dielectric thin-film waveguide, and its algorithm is presented. In this method, the Rayleigh principle previously used in conventional mode-matching techniques is extended to the Fourier transform of the wave field in the boundary-value problem for an unbounded object. Successful numerical results for dispersion relations and field distributions in a rib waveguide are obtained in detail, accompanied by their error estimations. The propagation characteristics, including field distributions in an optical rib waveguide, are investigated precisely for several geometries, and it is confirmed that the mode-matching method is accurate and effective for numerical analysis, not only with bounded objects, but also with unbounded objects such as thin-film waveguides in integrated optics.

Numerical analysis of a thin-film waveguide by mode-matching method

This paper proposes that EMG patterns can be analyzed and classified by neural networks. Through experiments and on-line simulations, it Is demonstrated that recognition of not only finger movement but also joint angles in continuously finger movement, based on EMG patterns, can be successfully accomplished. We also demonstrate a EMG controlled 5 fingers 10 joints robot hand.

Katsunori Shimohara

Papers

EMG pattern analysis and classification by neural network

EEG topography recognition by neural networks

Development and Evolution of Hardware Behaviors

Numerical analysis of a thin-film waveguide by mode-matching method

EMG pattern recognition by neural networks for multi fingers control