Sensor Fusion Quality Of Data Determination

Abstract

An unknown state value in a structure neuron value in a neural network, in one embodiment, is determined by using the difference between known values and output at an equivalent model location. The accuracy of model produced values with known values are determined compared to the known values. How much the known model produced locations were used to determine the unknown state value is determined. These amounts and accuracy of the model produced values are used to determine accuracy of the model produced value of the unknown state value.

We claim: 
     
         1 . A method for computing neuron accuracy implemented by one or more computers comprising:
 running a neural network with test neurons and a target neuron using known sensor values at test neurons for a cost function to produce modeled test neuron values and a modeled value of the target neuron;   comparing modeled test values to known sensor values, to determine quality of test neuron values;   calculating connection strengths of each test value relative to the target neuron; and   calculating accuracy of the target neuron using:
 quality of the test neuron values, and 
 connection strengths between the target neuron and the test neurons. 
   
     
     
         2 . The method of  claim 1 , wherein running the neural network comprises using state time series values as input into the neural network for a running period. 
     
     
         3 . The method of  claim 2 , wherein the state time series values are weather values affecting a controlled space. 
     
     
         4 . The method of  claim 3 , wherein the cost function compares the known sensor values to the modeled test values. 
     
     
         5 . The method of  claim 4 , wherein calculating connection strength comprises using automatic differentiated vector gradients. 
     
     
         6 . The method of  claim 5 , wherein calculating accuracy of the target neuron comprises matrix multiplying the quality of test neuron values by connection strengths between the target neuron and the test neurons. (be sure to mention that matrix multiplication works just like the dot product here.) 
     
     
         7 . The method of  claim 1 , wherein running the neural network comprises using machine learning techniques to determine connection strengths between the target neuron and the test neurons comprises using automatic differentiation to backpropagate from the target neuron to the test neurons. 
     
     
         8 . The method of  claim 1 , wherein the neural network is a heterogenous neural network. 
     
     
         9 . The method of  claim 1 , wherein at least one test neuron has an accuracy and an associated sensor, and where the test neuron accuracy relates to accuracy of the associated sensor. 
     
     
         10 . The method of  claim 1 , wherein the neural network has internal values, and further comprising warming up the neural network using at least a portion of an initial state time series values to modify the neural network internal values. 
     
     
         11 . The method of  claim 10 , further comprising warming up the the neural network by pre-running the neural network using successively larger portions of an input wave form until a goal state is reached. 
     
     
         12 . The method of  claim 1 , wherein the neural network models a controlled system, and wherein the controlled system comprises a controlled building system, a process control system, an HVAC system, an energy system, or an irrigation system. 
     
     
         13 . A system for computing neuron accuracy comprising: a processor; a memory in operational communication with the processor;
 a neural network which resides at least partially in the memory, the neural network comprising test neurons with test values and at least one target neuron with a target neuron value;   a neural network optimizer that optimizes the neural network using known sensor values and test values for a cost function to produce a solved neural network with modeled test values;   a determiner that determines quality of the test neuron values by comparing test neuron values in the solved neural network to corresponding actual values;   a machine learner that uses machine learning techniques to calculate connection strengths between the test neurons and the at least one target neuron; and   a function calculator that calculates accuracy of the at least one target neuron value using:
 quality of the test neuron values, and 
 connection strengths between the target neurons and the at least one test neuron. 
   
     
     
         14 . The system of  claim 13 , wherein the function calculator comprises matrix multiplying the quality of test neuron values by connection strengths between the target neuron and the test neurons. 
     
     
         15 . The system of  claim 13 , wherein at least one corresponding actual value comprises a sensor state value. 
     
     
         16 . The system of  claim 15 , wherein the sensor state value is derived from a sensor in a controlled space. 
     
     
         17 . The system of  claim 16 , further comprises an initializer, which uses state time series values as input into the neural network for a running period. 
     
     
         18 . The system of  claim 17 , wherein at least one of the machine learning techniques uses automatic differentiation to calculate connection strengths. 
     
     
         19 . A computer-readable storage medium configured with data and instructions which upon execution by a processor perform a method for computing neuron accuracy, the method comprising: initializing values for at least some test neurons in a neural network, the test neurons representing corresponding actual values;
 specifying a target neuron in the neural network;   optimizing the neural network using the actual values producing a solved neural network with a target neuron value and test neuron values;   using machine learning techniques to determine connection strengths between the target neuron and the test neurons;   determining quality of the test neuron values by comparing test neuron values in the solved neural network to corresponding actual neuron values; and   calculating accuracy of the target neuron using:
 quality of the test neuron values, and 
 connection strengths between the target neuron and the at least one test neuron. 
   
     
     
         20 . The computer-readable storage medium of  claim 19 , wherein the corresponding actual values are sensor values that correspond to test neuron locations.