phymath999: brain NEURAL COMPUTING The Structure of the Brain threshold value dirac “冲击函数”。在t时间内对一物体作用F的力 ...麦克斯韦方程组本来是电磁场关于场源的隐式函数形式

Static and Dynamic Neural Networks: From Fundamentals to ...

https://books.google.com.hk/books?isbn=0471460923

Madan Gupta, ‎Liang Jin, ‎Noriyasu Homma - 2004 - ‎Computers

... Computational energy function, 357 Continuous-time, 346 — dynamic neural ... 21 1 Diffeomorphism, 388 Dirac's delta function, 582 Direct inverse control

In linguistics, a quantized expression is such that, whenever it is true of some entity, it is not true of any proper subparts of that entity

phymath999: 微观物理量在某个微观运动态下的时间平均应该 ...

phymath999.blogspot.com/2014/.../blog-post_5741.ht...
Translate this page

Jan 20, 2014 - 所以物理量之间的乘法是这些系数(作为谱线集上的函数)之间的卷积(卷积 ... 对群胚上的函数也可以类似地定义卷积, 但这个卷积再也不是交换的 ...... 麦克斯韦方程组本来是电磁场关于场源的隐式函数形式 · "麦克斯韦方程组隐函数" .

http://www.tandfonline.com/doi/pdf/10.1080/10473289.2000.10464039

NEURAL COMPUTING The Structure of the Brain The approach of neural computing is to apply to computer systems the guiding principles that underlie the brain’s solution to problems.9 We know that the brain is organized in layered structures and uses many slow units that are highly interconnected (parallel). The human brain contains approximately 10,000 million (1010) basic units called neurons that are highly interconnected—every neuron is connected to about 10,000 (104) others. The neuron accepts many inputs, which are all added up in some (i.e., nonlinear) fashion, and produces an output if this sum is greater than some value, known as the threshold value. The inputs to the neuron arrive via chemical connections called synapses. These junctions alter the effectiveness of the transmitted signal. If enough active inputs are received at once, then the neuron will be activated and produce an output; otherwise, it will remain in its inactive state. Learning occurs when the effective coupling between one cell and another is modified.

NEURAL COMPUTING The Structure of the Brain The approach of neural computing is to apply to computer systems the guiding principles that underlie the brain’s solution to problems.9 We know that the brain is organized in layered structures and uses many slow units that are highly interconnected (parallel). The human brain contains approximately 10,000 million (1010) basic units called neurons that are highly interconnected—every neuron is connected to about 10,000 (104) others. The neuron accepts many inputs, which are all added up in some (i.e., nonlinear) fashion, and produces an output if this sum is greater than some value, known as the threshold value. The inputs to the neuron arrive via chemical connections called synapses. These junctions alter the effectiveness of the transmitted signal. If enough active inputs are received at once, then the neuron will be activated and produce an output; otherwise, it will remain in its inactive state. Learning occurs when the effective coupling between one cell and another is modified. Modeling the Brain The model neuron (Figure 1) must capture the biological neuron’s important features; perform a weighted sum of its inputs xi , compare this to some internal threshold level, and turn on only if this level is exceeded. If we call the output y, we can write y = f [∑wijxi ] where f is a monotonic increasing function called the thresholding function. In turn, this response serves as an input signal to other neurons. Artificial NN models can be constructed by suggesting different ways of connecting processing elements organized in one or more layers. The simpler feedforward network has two layers in which a set of input patterns arriving at an input layer (involving input units) are mapped directly to a set of output patterns at an output layer (involving output units). Such networks can only map similar input patterns to similar output patterns. Whenever the similarity structure of the input and output patterns are very different, only a network with internal representation (hidden units) can perform the necessary mappings.10 NNs of this form (multilayer feedforward) are illustrated in Figure 2 and can realize any arbitrarily complicated and non-linear functional relationship between its inputs and its outputs.11 The NNs employed here are trained by supervised learning, which is achieved in two stages. In the training

stage, the network is provided with a training data set (input plus desired output) and by implementing specific iterative algorithms, which adjust the coupling (synaptic weights) between neurons, the network is able to reproduce these examples. When the training stage has been completed, the values of the synaptic weights are fixed and a test set of unknown records is presented to the NN (testing stage). The Backpropagation Algorithm Suppose we have a multilayered feedforward NN with one input layer, N hidden layers, and one layer of output units. We use the following notation: Aip (m) = unit’s actual output; Tip = unit’s target output; Wij (m) = synaptic weights; and δjp (m) = learning parameter; where the superscript (m) denotes a layer within the structure of the NN (m = 0 for the input layer, m = 1,2,...,N for the hidden layers, m = N + 1 for the output layer), subscripts i and j label a unit within a layer, and subscript p labels the input patterns. Finally, thresholds are considered as weights emanating from units of constant output equal to one. The equation describing the node output can then be written as

phymath999

Tuesday, February 9, 2016

brain NEURAL COMPUTING The Structure of the Brain threshold value dirac “冲击函数”。在t时间内对一物体作用F的力 ...麦克斯韦方程组本来是电磁场关于场源的隐式函数形式 · "麦克斯韦方程组隐函数" .

Static and Dynamic Neural Networks: From Fundamentals to ...

phymath999: 微观物理量在某个微观运动态下的时间平均应该 ...

phymath999: dirac “冲击函数”。在t时间内对一物体作用F的力 ...

phymath999: Dirac-Delta函数,黎曼和无效, 这个函数列在n趋 ...

phymath999: delta函数没有斯切尔斯底积分，但是有勒贝格 ...

phymath999: 卷积convolution 冲量这一物理现象很能说明 ...

phymath999: “卷积”的物理意义痛苦程度会叠加(人（线性时不 ...

phymath999: 变分法延拓到多维空间曲面面积的定义：曲面微 ...

phymath999: "相關函數格林函數"量子场论

phymath999: dirac convolution 卷积傅立叶变换在这里的物理 ...

phymath999

No comments:

Post a Comment