Creating a Neural Network Node :

• use : NN NODE = new NN(structure) ---Where Structure is int[] array to specify the Neural Network structure.
• NN class constructor takes :
  • param :
    • NN_config type =>int[]
    • learning Rate ( Default = 1) => double
• For example :
  • int[] Structure = {7, 7, 7,7,1} will create a a Neural Network Node with & Neurons in each layer , including input layer and 1 neuron in output layer.
• use : NN.VALID_input_checker(Training inputs, Traingin outputs) for validating the structure of input dataset for before feeding it to TRAIN() function.
• use : NN.TRAIN(trainIn, trainOut, iteration , batches, normlization) for training the Neural Netowrk Node.
  • param :
    • trainIn : Data input for training purpose type => double[][]
    • trainOut : Data output for error check and updating weights type => double[][]
    • iteration : number of iteration for training type => int
    • batches : number of dataset batches to create for batch training type => int
    • normalization : True for normalized value of gradient type => boolean
• use : NN.TEST( testIn, testOut) for testing the Neural Network node.
  • param :
    • testIn : Data input for testing purpose type => double[][]
    • testOut : Data output for calculating the error rate type => double[][]
• use : NN.PREDICT(Unseen data to predict the output of new data instance.
  • param :
    • Unseen_Data : Unseen data instance type => doulbe[][]
  • output :
    • return type => ArrayList<double[][]> note : can be changed to double[][] if needed

NOTE : Each NN class instance saves it's own stats for Neural Network structure. Each NN node can have custom structure (# layers, #n neurons).

Outputs from different NN nodes can be used as inputs for other already trained NN nodes. NN class makes this trivial buy matching the data strucutre for input and output of a NN nodes. ( input => double[][] and output => doulbe[][])

• input param type => double[][]

• output param type => double[][]

• param type :
  • NN_config type => int[]
  • rand type => Random

• Available Methods :
  • transpose
  • multiply
  • getRow
  • getCol
  • dotProduct
  • add
  • subtract
  • normalize
  • randomizeArray

• Column1 => Distance detected by Sensor1
• Column2 => Distance detected by Sensor2
• Column3 => Distance detecetd by Sensor3
• Column4 => Navigational output :
  • 1 => Turn left
  • 2 => Go Straight
  • 3 => Turn Right

• Case 1 : Object is straight ahead of the sensor at distance L Object is visible in all 3 sensors ( Object is close to robot's all 3 sensors) 10.0-18.0 units with step 4.0 units -> 50 measurements Navigational output : 3 - Turn Right

• Case 2 : Object is straight ahead of the sensor at distance L Object is visible in all 3 sensors( Object is far from robot's all 3 sensors) 20.0-145.0 units with step 25.0 units -> 50 measurements Navigational output : 2 - Go Straight

• Case 3 : Object is left side of the robot at distance L Object is visible only in the left most sensor ( Object is close to robot's leftmost sensors) 3.0-18.0 unit with step 5.0 unit -> 50 measurements Navigational output : 3 - Turn Right

• Case 4 : Object is left side of the robot at distance L Object is visible only in the left most sensor ( Object is far from robo's leftmost sensors') 20.0-70.0 unit with step 10.0 unit -> 50 measurements Navigational output : 2- Go Straight

• Case 5 : Object is right side of the robot at distance L Object is visible only in the rightmost sensor ( Object is close to the robot's rightmost sensor) 3.0-18.0 unit with step 5.0 unit -> 50 measurements Navigational ouput : 1 - Turn Left

• Case 6 : Object is right side of the robot at distance L Object is visible only in the rightmost sensor ( Object is far from the robot's rightmost sensor) 20.0-70.0 unit with step 5.0 unit -> 50 measurements Navigational ouput : 2 - Go Straight