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