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