A) feedforward or feedback B) feedforward and feedback C) feedback manner D) feedforward manner
A) output layer B) hidden layer C) input layer D) second layer
A) receives inputs from all others B) gives output to all others C) may receive or give input or output to others
A) Supervised and Unsupervised B) UnSupervised C) Supervised
A) Adaptive Resonance Theory B) Artificial Resonance Theory C) Automatic Resonance Theory
A) Bipolar B) Binary and Bipolar C) Binary
A) Large Cluster B) Small cluster C) No change
A) feedforwward network with hidden layer B) two feedforward network with hidden layer C) feed forward network only
A) its ability to learn inverse mapping functions B) its ability to learn forward mapping functions C) its ability to learn forward and inverse mapping functions
A) each input unit is connected to each output unit B) all are one to one connected C) some are connected
A) Supervised B) Learning with critic C) UnSupervised
A) TRUE B) FALSE
A) inhibitory inpur B) excitatory input
A) both deterministically & stochastically B) deterministically C) stochastically
A) greater the degradation less is the activation value of other units B) greater the degradation less is the activation value of winning units C) greater the degradation more is the activation value of winning units
A) Yes B) depends on type of clustering C) No
A) learning laws which modulate difference between synaptic weight & output signal B) learning laws which modulate difference between actual output & desired output C) learning laws which modulate difference between synaptic weight & activation value
A) the overall characteristics of the mapping problem B) the number of inputs C) the number of outputs
A) the number of patterns that can be stored B) the number of inputs it can deliver C) the number of inputs it can take
A) can be slow or fast in general B) Fast process C) Slow process |