Neural networks are wide topic. But this small example demonstrates how to create basic neural sensor which takes resistive readings from multiply sensors and multiply it by wight factor and then sum the results. Results are compared to three level threshold. Without going too deep in to neural networks we can say, that neural cell thresholds are similar to natural biological neurons. For instance pain levels: itch is low level of pain while burning sensations is combined of ice cold and warm sensations. Neural sensors can operate in the same way.

Lets take typical neural sensors which consists of two inputs with some weights and three outputs. Depending on threshold level that sum of inputs gives – we have and output signal on one of three outputs.

Lets build real world neuron with two inputs and three outputs. As sensor I will use potentiometers which can be replaced by photocells or temperature sensors. According to these two sensor readings neuron can have three states:

• low
• medium
• high

this could be like low light, medium light and high light, o low temp and so on. If you will decide to put neuron to your robot, this could be used for solar cell charging like if its is dark, go to sleep mode, if it is medium light go to brightest area, if it is high light level, stop and start charging.

Each decision is indicated by one of three LEDs.

Above schematic is simple neuron sensor simulator made of Atmega8 MCU couple potentiometers and three LEDs. Lets write simple routine to simulate this embedded neuron. For simulation I used VMLAB simulator.

In the example you may see that one input has weight of 2 and second of 3 units. The exit selected by comparing w value with thresholds.

Simulation results are here:

This way using simple neural nets you can build more complex and more smart systems.

Download VMLAB neuron demo project .

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This entry was posted on Monday, November 6th, 2006 at 2:08 pm and is filed under AVR Microcontrollers, Embedded General. You can follow any responses to this entry through the RSS 2.0 feed. You can leave a response, or trackback from your own site.