【導(dǎo)讀】重要的現(xiàn)實(shí)意義。隨著人工智能特別是神經(jīng)網(wǎng)絡(luò)技術(shù)的發(fā)展，為傳感器溫度補(bǔ)償?shù)乃?。法提供了新的有效手段，?duì)于不同的算法，都具有自己的優(yōu)缺點(diǎn)。通過(guò)實(shí)驗(yàn)，將實(shí)驗(yàn)。其更好地應(yīng)用于實(shí)踐中。測(cè)試技術(shù)中將測(cè)試分為電參數(shù)的測(cè)量與非電參數(shù)的測(cè)量。功率、頻率、阻抗、波形等，這些參量都是表征系統(tǒng)或設(shè)備性能的。在生活實(shí)踐中，經(jīng)常遇到的是非電量的測(cè)量。現(xiàn)在非電量的測(cè)量大部分是

　　

【正文】 on as an input and is multiplied by a scalar bias b. The input to the transfer function f is n, the sum of the bias b and the 21 product Wp. This sum is passed to the transfer function f to get the neuron’s output a, which in this case is a scalar. Note that if we had more than one neuron, the work output would be a vector. A layer of a work is defined in the figure shown above. A layer includes the bination of the weights, the multiplication and summing operation (here realized as a vector product Wp), the bias b, and the transfer function f. The array of inputs, vector p, is not included in or called a layer. Each time this abbreviated work notation is used, the size of the matrices will be shown just below their matrix variable names. We hope that this notation will allow you to understand the architectures and follow the matrix mathematics associated with them. As discussed previously, when a specific transfer function is to be used in a figure, the symbol for that transfer function will replace the f shown above. 4 Summary The inputs to a neuron include its bias and the sum of its weighted inputs(using the inner product). The output of a neuron depends on the neuron’s inputs and on its transfer function. There are many useful transfer single neuron cannot do very much. However, several neurons can bebined into a layer or multiple layers that have great power. Hopefully thistoolbox makes it easy to create and understand such large works. The architecture of a work consists of a description of how many layers a work has, the number of neurons in each layer, each layer’s transfer function, and how the layers connect to each other. The best architecture to use depends on the type of problem to be represented by the work. A work effects a putation by mapping input values to output values. The particular mapping problem to be performed fixes the number of inputs, as well as the number of outputs for the work. Aside from the number of neurons in a work’s output layer, the number of neurons in each layer is up to the designer. Except for purely linear works, the more neurons in a hidden layer, the more powerful the work. If a linear mapping needs to be represented linear neurons should be , linear works cannot perform any nonlinear putation. Use of a nonlinear transfer function makes a work capable of storing nonlinear relationships between input and output. 22 A very simple problem can be represented by a single layer of , singlelayer works cannot solve certain problems. Multiple feedforward layers give a work greater freedom. For example, any reasonable function can be represented with a twolayer work: a sigmoid layer feeding a linear output with biases can represent relationships between inputs and outputsmore easily than works without biases. (For example, a neuron without a bias will always have a input to the transfer function of zero when all of its inputs are zero. However, a neuron with a bias can learn to have any transfer function input under the same conditions by learning an appropriate value for the bias.) 5 Benefits of Using Temperature Compensation Many system peripherals contain information on how the performance of the peripheral changes as the temperature changes. For example, the Freescale MPX10 series of unpensated 10 kPa pressure sensors contain information regarding how this sensor behaves across its operating temperature range. Many system peripherals contain information on how the performance of the 。 This information is used to create temperature pensation described in AN840 Temperature Compensation Methods that can be found at . Sensors with internal pensation are also available,but at a higher cost. The benefit of lower overall system cost can be achieved by using a method where the internal temperature sensor of the S08 microcontroller is used as the reference to perform temperature pensation. Another benefit of using temperature pensation is extending the operating range of the end application. For example an application that requires accurate serial munications requires an accurate reference clock. If the reference clock loses accuracy at a hot temperature, the end application can not acplish the serial munications. Using temperature pensation to modify the serial munications at a hot temperature allows the end application to operate across a wider range. Both of the benefits listed above add value to the end application by using only the available on chip features. 6 Temperatue Compensation Methods The theory behind performing temperature pensation is straight forward. It involves 23 taking a temperature reading and changing the peripheral parameters based on a knowledge base of how the peripheral performs across temperature. Peripheral parameters are external or internal factors that affect the operation of the peripheral. In the case of an internal reference clock, such as the ICS internal reference clock on many S08 microcontrollers, the peripheral parameter that affects the output frequency is the TRIM register. Many system peripherals contain a trim register that allows pensation of the output of the peripheral. Another example of a peripheral parameter are equation variables. For example, if the peripheral is a sensor that outputs an analog voltage and the output contains an offset that is temperature dependant, then the peripheral parameter that must be changed at different temperatures are the equation variables that account for the offset voltage. To understand the peripheral parameters and how they must be changed, a knowledge base must be referenced. An example of a knowledge base is a specification. For example, the specification for the Freescale MPX10 series contains the parameters that define how the output of the pressure sensor changes across temperature ranges.