Spiking Neuron Network Adaptive Control for Mixed Sensor Inputs
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Solution Overview
Problem
Existing robotic control systems using spiking neuron networks face challenges in effectively encoding and processing continuous sensory inputs of varying dynamical range and nature, leading to inefficiencies in sensor upgrades and changes, and requiring labeled inputs, which can result in system malfunction if inputs are mixed or sensors degrade.
Innovation Solution
An adaptive controller apparatus utilizing a spiking neuron network with a continuous-to-spiking expansion kernel, comprising multiple spiking neurons with receptive fields, encodes continuous input signals into spiking outputs and adjusts connection efficacies based on reinforcement signals to generate control signals through a reinforcement learning process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional spiking neuron networks use fixed encoding mechanisms for sensory inputs, then the system structure remains simple, but the system cannot adapt to sensor upgrades or changes without reconfiguration
Solution Approach 1:
The encoder block is designed with a universal continuous-to-spiking expansion kernel that can process various types of continuous sensory inputs (accelerometer, gyroscope, magnetometer, barometer, microphone, camera, GPS) through a unified encoding mechanism. This single encoder structure replaces multiple specialized encoders, enabling the system to adapt to different sensor types without structural changes while maintaining simplicity.
2Measurement precision
If the controller requires labeled inputs for proper processing, then processing accuracy improves, but system reliability decreases when inputs are mixed or sensors degrade
Solution Approach 1:
The spiking neuron network controller performs self-identification of input sources through its adaptive learning mechanism. The network automatically determines which sensors are functioning and how to weight their inputs based on current performance, eliminating the need for external labeling or manual configuration. This self-service capability ensures continuous reliable operation even when some sensors degrade or fail.
Solution Approach 2:
The system implements continuous feedback through the reinforcement learning process, where the controller monitors the quality and reliability of sensor inputs in real-time. Based on feedback from system performance and sensor behavior, the network dynamically adjusts connection efficacies to compensate for sensor degradation or mixing, maintaining reliable operation without requiring labeled inputs.
3Adaptability or versatility
If the system uses fixed connection efficacies in the spiking neuron network, then the control logic remains simple, but the system cannot adapt to changing performance requirements
Solution Approach 1:
The spiking neuron network implements dynamic adaptation through reinforcement learning, where connection efficacies between neurons are continuously adjusted based on system performance feedback. This dynamic mechanism allows the controller to automatically optimize its behavior for different operating conditions and performance requirements without requiring complex pre-programmed control logic or manual reconfiguration.
Data Source
AI summary
Adaptive controller apparatus of a plant may be implemented. The controller may comprise an encoder block and a control block. The encoder may utilize basis function kernel expansion technique to encode an arbitrary combination of inputs into spike output. The controller may comprise spiking neuron network operable according to reinforcement learning process. The network may receive the encoder output via a plurality of plastic connections. The process may be configured to adaptively modify connection weights in order to maximize process performance, associated with a target outcome. The relevant features of the input may be identified and used for enabling the controlled plant to achieve the target outcome.