The deep integration of edge computing and Artificial Intelligence (AI) in IoT (Internet of Things)-enabled smart cities has given rise to new edge AI paradigms that are more vulnerable to attacks such as data and model poisoning and evasion of attacks. This work proposes an online poisoning attack framework based on the edge AI environment of IoT-enabled smart cities, which takes into account the limited storage space and proposes a rehearsal-based buffer mechanism to manipulate the model by incrementally polluting the sample data stream that arrives at the appropriately sized cache. A maximum-gradient-based sample selection strategy is presented, which converts the operation of traversing historical sample gradients into an online iterative computation method to overcome the problem of periodic overwriting of the sample data cache after training. Additionally, a maximum-loss-based sample pollution strategy is proposed to solve the problem of each poisoning sample being updated only once in basic online attacks, transforming the bi-level optimization problem from offline mode to online mode. Finally, the proposed online gray-box poisoning attack algorithms are implemented and evaluated on edge devices of IoT-enabled smart cities using an online data stream simulated with offline open-grid datasets. The results show that the proposed method outperforms the existing baseline methods in both attack effectiveness and overhead.
Citation: Yanxu Zhu, Hong Wen, Jinsong Wu, Runhui Zhao. Online data poisoning attack against edge AI paradigm for IoT-enabled smart city[J]. Mathematical Biosciences and Engineering, 2023, 20(10): 17726-17746. doi: 10.3934/mbe.2023788
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The deep integration of edge computing and Artificial Intelligence (AI) in IoT (Internet of Things)-enabled smart cities has given rise to new edge AI paradigms that are more vulnerable to attacks such as data and model poisoning and evasion of attacks. This work proposes an online poisoning attack framework based on the edge AI environment of IoT-enabled smart cities, which takes into account the limited storage space and proposes a rehearsal-based buffer mechanism to manipulate the model by incrementally polluting the sample data stream that arrives at the appropriately sized cache. A maximum-gradient-based sample selection strategy is presented, which converts the operation of traversing historical sample gradients into an online iterative computation method to overcome the problem of periodic overwriting of the sample data cache after training. Additionally, a maximum-loss-based sample pollution strategy is proposed to solve the problem of each poisoning sample being updated only once in basic online attacks, transforming the bi-level optimization problem from offline mode to online mode. Finally, the proposed online gray-box poisoning attack algorithms are implemented and evaluated on edge devices of IoT-enabled smart cities using an online data stream simulated with offline open-grid datasets. The results show that the proposed method outperforms the existing baseline methods in both attack effectiveness and overhead.
Many differential equations have been proposed (see [8,11,13], [17]-[19], [21]-[22], [24,27] and references therein) to model the dynamic changes of substrate concentration and product one in enzyme-catalyzed reactions. Among those models, a typical form ([7]) is the following skeletal system
{˙x=v−V1(x,y)−V3(x),˙y=q(V1(x,y)−V2(y)), | (1) |
where
V1(0,y)=0, ∂V1/∂x>0, ∂V1/∂y>0, V2(y)≥0, ∀x,y>0, |
and
The case that
{˙x=1−xmyn−lx,˙y=q(xmyn−y), |
called the multi-molecular reaction model sometimes, where
{˙x=v−γxmyn−βx,˙y=γxmyn−v2yμ2+y, |
where
{˙x=v−V1(x,y)−v3xu3+x,˙y=q(V1(x,y)−v2yu2+y) |
with
{˙x=v−v1xy−v3xu3+x,˙y=q(v1xy−v2yu2+y) | (2) |
with
{˙x=a−xy−bx1+x,˙y=κy(x−c1+y), | (3) |
where we still use
{˙x=(1+y){(1+x)(a−xy)−bx},˙y=κ(1+x)y{(1+y)x−c}, | (4) |
in the first quadrant
In this paper we continue the work of [27] to give conditions for the existence of a cusp and compute the parameter curves for the Bogdanov-Takens bifurcation, which induces the appearance of homoclinic orbits and periodic orbits, indicating the tendency to steady-states or a rise of periodic oscillations for the concentrations of the substrate and product.
It is proved in [27] that system (4) has at most 3 equilibria, i.e.,
p1:=−12{(a−b−c+1)−[(a−b−c+1)2−4(a−c)]1/2},p2:=−12{(a−b−c+1)+[(a−b−c+1)2−4(a−c)]1/2}. | (5) |
Moreover, if
TE0:={(a,b,c,κ)∈R4+|a=bc/(1+c),b≠(c+1)2}:=4⋃i=1T(i)E0,PE0:={(a,b,c,κ)∈R4+|a=bc/(1+c),b=(c+1)2},HE1:={(a,b,c,κ)∈R4+|κ=κ1,bc/(1+c)<a<c,0<b≤1}∪{(a,b,c,κ)∈R4+|κ=κ1,bc/(1+c)<a<c+(b1/2−1)2,1<b<(c+1)2},SNE∗:={(a,b,c,κ)∈R4+|a=a∗, 1<b<(c+1)2,κ≠κ∗}:=4⋃i=1SN(i)E∗,B1:={(a,b,c,κ)∈R4+|a=c},B2:={(a,b,c,κ)∈R4+|a=b}, |
which divide
R1:={(a,b,c,κ)∈R4+|c<a<a∗,1<b<c,c>1, or b<a<a∗,c<b<(c+1)2/4,c>1},R2:={(a,b,c,κ)∈R4+|b<a<c,0<b<c}R3:={(a,b,c,κ)∈R4+|bc/(1+c)<a<b,0<b<c or bc/(1+c)<a<c,c<b<c+1},R4:={(a,b,c,κ)∈R4+|0<a<bc/(1+c),0<b<c+1 or 0<a<c,b>c+1},R5:={(a,b,c,κ)∈R4+|c<a<bc/(1+c),b>c+1},R6:={(a,b,c,κ)∈R4|c<a<b,c<b<(c+1),c>3 or bc/(1+c)<a<b,c+1<b<(c+1)2/4,c>3 or bc/(1+c)<a<a∗,(c+1)2/4<b<(c+1)2,c>3 or c<a<b,c<b<(c+1)2/4,1<c≤3 or c<a<a∗,(c+1)2/4<b<c+1,1<c≤3 or bc/(1+c)<a<c+(b1/2−1)2,(c+1)<b<(c+1)2,c≤3 or c<a<a∗,1<b<c+1,c≤1},R7:={(a,b,c,κ)∈R4+|c+(b1/2−1)2<a<b,(c+1)2/4<b<(c+1)2,c>1 or bc/(1+c)<a<b,b>(c+1)2 or c<a<b,c<b<1,c≤1 or c+(b1/2−1)2<a<b,1<b<(c+1)2,c≤1},R0:=R4+∖{PE0∪SNE∗∪TE0∪(2⋃i=1Bi)∪B∪(7⋃i=1Ri)}, |
where
T(1)E0:={(a,b,c,κ)∈R4+|a=bc/(1+c),0<b<c+1},T(2)E0:={(a,b,c,κ)∈R4+|a=bc/(1+c),c+1<b<(c+1)2},T(3)E0:={(a,b,c,κ)∈R4+|a=bc/(1+c),b>(c+1)2},T(4)E0:={(a,b,c,κ)∈R4+|a=bc/(1+c),b=c+1},SN(1)E∗:={(a,b,c,κ)∈R4+|a=a∗, 1<b<(c+1)2/4,c>1,κ≠κ∗},SN(2)E∗:={(a,b,c,κ)∈R4+|a=a∗, b=(c+1)2/4,c>1,κ≠κ∗},SN(3)E∗:={(a,b,c,κ)∈R4+|a=a∗, (c+1)2/4<b<(c+1)2,c>1,κ≠κ∗},SN(4)E∗:={(a,b,c,κ)∈R4+|a=a∗, 1<b<(c+1)2,c≤1,κ≠κ∗},κ1:=p−21{(p1+1)(c−p1)}−1c{p1(c−p1)+a},κ∗:=(c−b1/2+1)−1(b1/2−1)−2c2. | (6) |
The following lemma is a summary of Theorems 1, 2 and 3 of [27].
Lemma 2.1. (ⅰ) System (4) has a saddle-node
(ⅱ) System (4) has a weak focus
H(2)E1:={(a,b,c,κ)∈HE1:2p1(p1+1)a3+{(p21+p1+1)c2+p1(2p21+p1−2)c−3p31(p1+1)}a2−(c−p1){(p31+3p21+p1+1)c2+2p21(p21+3p1+3)c+3p41(p1+1)}a+p21{(p1+2)c+p21}{c−p1(p1+1)}(c−p1)2=0}. |
(ⅲ) System (4) has a saddle-node
The above Lemma 2.1 does not consider parameters in the set
B:={(a,b,c,κ)∈R4+|a=a∗, 1<b<(c+1)2,κ=κ∗}, | (7) |
where
This paper is devoted to bifurcations in
Lemma 2.2. If
C:={(a,b,c,κ)∈B|c=ς(b):=14b1/2(b1/2−1){b1/2+2+(17b−12b1/2+4)1/2}}, |
then equilibrium
Proof. For simplicity in statements, we use the notation
p:=b1/2−1. | (8) |
For
{˙x=y+c(p2+cp+c)p3x2+1p+1xy−pc2(p+1)y2−c(p2+c)p4x3−p2+2pc+2cp2c(p+1)x2y−2p+1c2(p+1)2xy2−c2p4x4−2p2(p+1)x3y−1c2(p+1)2x2y2,˙y=−c3(p+1)p3x2−c2(p+1)p2(c−p)xy−1c−py2−(p+1)(p2+c)p5(c−p)x3−c(p2+2pc+2c)p3(c−p)x2y−2p+1p(p+1)(c−p)xy2−c4(p+1)p5(c−p)x4−2c2p3(c−p)x3y−1p(p+1)(c−p)x2y2, | (9) |
by translating
{˙u=v,˙v=−c3(p+1)p3u2+c{(2p+2)c2−(p2+3p)c−2p3}p3(c−p)uv+c−2p−1(p+1)(c−p)v2+c3(p2+c)p4(c−p)u3−c{(p+1)(p+3)c2+p(p2−3p−3)c−p3(3p+2)}p4(p+1)(c−p)u2v−(5p2+8p+4)c+2p2(p+1)cp2(p+1)2uv2−1c2(p+1)v3−c2(c2+2p2c−p3)p5(c−p)u4+1p5(p+1)2(c−p){(p+4)(p+1)2c3+p(7p3+7p2−3p−4)c2−p3(8p2+15p+8)c−2p5(p+1)}u3v+(3p3+6p2+6p+2)c2+p(2p+1)(2p2+2p−1)c−p3(p+1)(7p+4)cp3(p+1)3(c−p)u2v2−(3p+4)c2−3p(p+2)c−2p3c3p(p+1)2(c−p)uv3−2c−3pc4(p+1)2(c−p)v4+O(|u,v|5). | (10) |
Since the linear part is nilpotent, by Theorem 8.4 in [14] system (10) is conjugated to the Bogdanov-Takens normal form, i.e., the right-hand side of the second equation is a sum of terms of the form
{˙u=v,˙v=−c3(p+1)p3u2+c{(2p+2)c2−(p2+3p)c−2p3}p3(c−p)uv+O(|u,v|3), | (11) |
where the term of
c2−p2+3p2(p+1)c−p3p+1=0, | (12) |
which comes from the numerator of the coefficient of
c=14(p+1)−1p{p+3+(17p2+22p+9)1/2}, |
which defines the function
In this section we discuss in the case that
Theorem 3.1. If
SN+:={(a,κ)∈U|a=a∗, κ>κ∗,0<c<ς(b)}∪{(a,κ)∈U|a=a∗, κ<κ∗,c>ς(b)},SN−:={(a,κ)∈U|a=a∗, κ<κ∗,0<c<ς(b)}∪{(a,κ)∈U|a=a∗, κ>κ∗,c>ς(b)},H:={(a,κ)∈U|a=a∗−((2b1/2+1)c2−((b1/2−1)2+3(b1/2−1))c −2(b1/2−1)3)−2b1/2(b1/2−1)6(c−b1/2+1)4(κ−κ∗)2+O(|κ−κ∗|3), κ>κ∗,0<c<ς(b)}∪{(a,κ)∈U|a=a∗−((2b1/2+1)c2−((b1/2−1)2+3(b1/2−1))c −2(b1/2−1)3)−2b1/2(b1/2−1)6(c−b1/2+1)4(κ−κ∗)2+O(|κ−κ∗|3), κ<κ∗,c>ς(b)},L:={(a,κ)∈U|a=a∗−49/25((2b1/2+1)c2−((b1/2−1)2+3(b1/2−1))c −2(b1/2−1)3)−2b1/2(b1/2−1)6(c−b1/2+1)4(κ−κ∗)2+O(|κ−κ∗|3), κ>κ∗,0<c<ς(b)}∪{(a,κ)∈U|a=a∗−49/25((2b1/2+1)c2−((b1/2−1)2+3(b1/2−1))c −2(b1/2−1)3)−2b1/2(b1/2−1)6(c−b1/2+1)4(κ−κ∗)2+O(|κ−κ∗|3), κ<κ∗,c>ς(b)}, |
such that system (4) produces a saddle-node bifurcation near
The above bifurcation curve
Proof. Let
ε1:=a−a∗,ε2:=κ−κ∗, | (13) |
and consider
{˙x=c(p+1)pε1+(−c2(p+1)p2+cpε1)x+(−c(p+1)+(p+1)ε1)y−c(c−p)p2x2+(−c(2+3p)p+ε1)xy−p(p+1)y2+O(‖(x,y)‖3),˙y=(c3(p+1)p4+c(p+1)(c−p)p2ε2)x+(c2(p+1)p2+(p+1)(c−p)ε2)y+(c3p4+c(c−p)p2ε2)x2+(c3(2+3p)−c2p(2p+1)(c−p)p3+c(3p+2)−p(2p+1)pε2)xy+(c2(p+1)(c−p)p+p(p+1)ε2)y2+O(‖(x,y)‖3). | (14) |
Introducing new variables
{˙ξ1=η1,˙η1=E00(ε1,ε2)+E10(ε1,ε2)ξ1+E20(ε1,ε2)ξ21+F(ξ1,ε1,ε2)η1+E02(ε1,ε2)η21, | (15) |
where
F(0,0,0)=0, ∂F∂ξ1(0,0,0)=E11(0,0)=(2p+2)(c2−p2+3p2(p+1)c−p3p+1)≠0. |
By the Implicit Function Theorem, there exists a function
ξ2=ξ1−ξ1(ε1,ε2),η2=η1 |
to vanish the term proportional to
{˙ξ2=η2,˙η2=ψ1(ε1,ε2)+ψ2(ε1,ε2)ξ2+E20(ε1,ε2)ξ22+E11(ε1,ε2)ξ2η2+E02(ε1,ε2)η22, | (16) |
where
ψ1(ε1,ε2):=E00(ε1,ε2)+E10(ε1,ε2)ξ1(ε1,ε2)+E20(ε1,ε2)ξ21(ε1,ε2),ψ2(ε1,ε2):=E10(ε1,ε2)+2ξ1(ε1,ε2)E20(ε1,ε2). |
In order to eliminate the
ξ3=ξ2, η3=η2−E02(ε1,ε2)ξ2η2 |
together with the time-rescaling
{˙ξ3=η3,˙η3=ζ1(ε1,ε2)+ζ2(ε1,ε2)ξ3+˜E20(ε1,ε2)ξ23+E11(ε1,ε2)ξ3η3, | (17) |
where
ζ1(ε1,ε2):=ψ1(ε1,ε2), ζ2(ε1,ε2):=ψ2(ε1,ε2)−ψ1(ε1,ε2)E02(ε1,ε2),˜E20(ε1,ε2):=E20(ε1,ε2)−E10(ε1,ε2)E02(ε1,ε2). |
Further, in order to reduce coefficient of
u=˜E20(ε1,ε2)E211(ε1,ε2)ξ3,v=sign(E11(ε1,ε2)˜E20(ε1,ε2))˜E220(ε1,ε2)E311(ε1,ε2), |
where
{˙u=v,˙v=ϕ1(ε1,ε2)+ϕ2(ε1,ε2)u+u2+ϑuv, | (18) |
where
ϕ1(ε1,ε2):=E411(ε1,ε2)˜E320(ε1,ε2)ζ1(ε1,ε2)={(2p+2)c2−(p2+3p)c−2p3}4ε1ϕ11(ε1,ε2)p4(c−p)4ϕ212(ε1,ε2), ϕ2(ε1,ε2):=E211(ε1,ε2)˜E220(ε1,ε2)ζ2(ε1,ε2)=√2{(2p+2)c2−(p2+3p)c−2p3}ϕ21(ε1,ε2)c3/2(c−p)2(p+1)1/2pϕ3/212(ε1,ε2), |
and polynomials
Let
μ1=ϕ1(ε1,ε2),μ2=ϕ2(ε1,ε2), | (19) |
where
|∂ϕ1(ε1,ε2)∂ε1∂ϕ1(ε1,ε2)∂ε2∂ϕ2(ε1,ε2)∂ε1∂ϕ2(ε1,ε2)∂ε2|(ε1,ε2)=(0,0)=−{(2p+2)c2−(p2+3p)c−2p3}5p6c4(c−p)4(p+1)≠0, | (20) |
implying that (19) is a locally invertible transformation of parameters. This transformation makes a local equivalence between system (18) and the versal unfolding system
{˙˜u=˜v,˙˜v=μ1+μ2˜u+˜u2+ϑ˜u˜v, | (21) |
where
SN+:={(μ1,μ2)∈V0 | μ1=0, μ2>0},SN−:={(μ1,μ2)∈V0 | μ1=0, μ2<0},H:={(μ1,μ2)∈V0 | μ1=−μ22, μ2>0},L:={(μ1,μ2)∈V0 | μ1=−4925μ22+o(|μ2|2), μ2>0}, | (22) |
where
In what follows, we present above bifurcation curves in parameters
ε1=ω1(μ1,μ2), ε2=ω2(μ1,μ2) | (23) |
in a small neighborhood of
μ1=ϕ1(ω1(μ1,μ2),ω2(μ1,μ2)), μ2=ϕ2(ω1(μ1,μ2),ω2(μ1,μ2)). | (24) |
Substitute the second order formal Taylor expansions of
ϕ1(ε1,ε2)={(2p+2)c2−(p2+3p)c−2p3}4ε1/{p6c2(c−p)4(p+1)}−{(2p+2)c2−(p2+3p)c−2p3}4(24p2c4+42c4p+21c4−8p3c3−54c3p2−44c3p−36c2p4−12p3c2+27p2c2+8p5c+32cp4+16p6)ε21/{2c4p8(c−p)6(p+1)2}−{(2p+2)c2−(p2+3p)c−2p3}4ε1ε2/{(c4p4(c−p)3(p+1)}+o(|ε1,ε2|2), | (25) |
ϕ2(ε1,ε2)={(2p+2)c2−(p2+3p)c−2p3}ε1/{2c2(p3−2cp+p2+c2p+c2−2cp2)p4}−{(2p+2)c2−(p2+3p)c−2p3}ε2/c2−{(2p+2)c2−(p2+3p)c−2p3}(−243p3c3+832p3c4+513p2c4+455p4c3−594p5c2−1347p3c5−1209p2c5+165p4c4+1138p5c3−324p6c2−424p7c−200p5c4+382p6c3+512p7c2−520cp8−396c5p−48p9+108c6−48p10+384c6p3+414c6p−104cp9+264c2p8+594c6p2−672c5p4+96c6p4−136c5p5−44c4p6−76c3p7)ε21/{4c3(p+1)2(c−p)4p6}−{(2p+2)c2−(p2+3p)c−2p3}(8p2c4+23c4p+12c4+30p3c3+8c3p2−22c3p−58c2p4−85p3c2+6p2c2−8p5c+46cp4+24p6)ε1ε2/{4c4p2(p+1)(c−p)2}+(c−p)p2{(2p+2)c2−(p2+3p)c−2p3}ε22/c4+o(|ε1,ε2|2). | (26) |
Then, comparing the coefficients of terms of the same degree in (24), we obtain the second order approximations
ε1=c2p6(c−p)4(p+1)μ1/{(2p+2)c2−(p2+3p)c−2p3}4+c2p10(c−p)6(p+1)(32p2c4+56c4p+27c4−16p3c3−79c3p2−59c3p−48c2p4−19p3c2+36p2c2+12p5c+50cp4+24p6)μ21/{2{(2p+2)c2−(p2+3p)c−2p3}8}+c2p8(c−p)5(p+1)μ1μ2/{(2p+2)c2−(p2+3p)c−2p3}5+o(|μ1,μ2|2), | (27) |
ε2=c2p2(c−p)2(−8p5−12cp4−18cp3+8c3p2−11p2c2−9c2p+14c3p+6c3)μ1/{2{(2p+2)c2−(p2+3p)c−2p3}4}−c2μ2/{(2p+2)c2−(p2+3p)c−2p3}+c2p6(c−p)4(1314c7p2+630pc7−270p3c4+2068p3c5+612p2c5+677p4c4−1134p5c3+4387p5c4−1056p6c3−1804p7c2−3741c6p3+756c5p4+1160c3p8−2268c6p4+1176c7p3−1272c5p6−352c6p5+384c7p4−320p11+108c7−704cp10+224c2p9−2046c5p5+4258c4p6+832p7c4−1464p8c2−2289c6p2+1544p7c3−450c6p−1344cp9)μ21/{8{(2p+2)c2−(p2+3p)c−2p3}8}+c2p4(c−p)2(40p2c4+61c4p+24c4−78p3c3−158c3p2−68c3p−14c2p4+43p3c2+48p2c2+32p5c+62cp4+24p6)μ1μ2/{4{(2p+2)c2−(p2+3p)c−2p3}5}+c2p2(c−p)μ22/{(2p+2)c2−(p2+3p)c−2p3}2+o(|μ1,μ2|2). | (28) |
Then we are ready to express those bifurcation curves in parameters
For curves
ε2=−c2(2p+2)Ψ(c)μ2+O(|μ2|2), | (29) |
where
SN+:={(ε1,ε2) | ε1=0,ε2>0,0<c<ς(b)}∪{(ε1,ε2) | ε1=0,ε2<0,c>ς(b)},SN−:={(ε1,ε2) | ε1=0,ε2<0,0<c<ς(b)}∪{(ε1,ε2) | ε1=0,ε2>0,c>ς(b)}. |
For curve
∂Υ∂ε1|(ε1,ε2)=(0,0)={(2p+2)Ψ(c)}4/{p6c2(c−p)4(p+1)}≠0. |
By the Implicit Function Theorem, there exists a unique
ε1=ϵ1(ε2)=−p6(c−p)44(p+1)Ψ2(c)ε22+o(|ε2|2). | (30) |
Further, replacing
ε2=−c2(2p+2)Ψ(c)μ2+o(|μ2|). |
Similarly to (29), from (22) we obtain that
H:={(ε1,ε2) | ε1=−p6(c−p)44(p+1)Ψ2(c)ε22+o(|ε2|2), ε2>0,0<c<ς(b)}∪{(ε1,ε2) | ε1=−p6(c−p)44(p+1)Ψ2(c)ε22+o(|ε2|2), ε2<0,c>ς(b)}. |
For curve
ε1=−49p6(c−p)4100(p+1)Ψ2(c)ε22+o(|ε2|2). |
Similarly to (29), from (22) we obtain that
L:={(ε1,ε2) | ε1=−49p6(c−p)4100(p+1)Ψ2(c)ε22+o(|ε2|2), ε2>0,0<c<ς(b)}∪{(ε1,ε2) | ε1=−49p6(c−p)4100(p+1)Ψ2(c)ε22+o(|ε2|2), ε2<0,c>ς(b)}. |
Finally, with the replacement (13) we can rewrite the above bifurcation curves
In this paper we analyzed the dynamics of system (4) near the equilibrium
More concretely, in this case,
a∗=(c+1)24, κ∗=8c2(c+1)(c−1)2. |
Moreover, the four bifurcation curves divide the neighborhood
DI:={(a,κ)∈U| a<(c+1)24, κ≤8c2(c+1)(c−1)2}⋃{(a,κ)∈U| a<(c+1)24−49(c−1)6(c+1)33200(2c2+c+1)2{κ−8c2(c+1)(c−1)2}2+O(|κ−8c2(c+1)(c−1)2|3), κ>8c2(c+1)(c−1)2},DII:={(a,κ)∈U| (c+1)24−49(c−1)6(c+1)33200(2c2+c+1)2{κ−8c2(c+1)(c−1)2}2+O(|κ−8c2(c+1)(c−1)2|3)<a<(c+1)24−(c−1)6(c+1)3128(2c2+c+1)2{κ−8c2(c+1)(c−1)2}2+O(|κ−8c2(c+1)(c−1)2|3), κ>8c2(c+1)(c−1)2},DIII:={(a,κ)∈U| (c+1)24−(c−1)6(c+1)3128(2c2+c+1)2{κ−8c2(c+1)(c−1)2}2+O(|κ−8c2(c+1)(c−1)2|3)<a<(c+1)24, κ>8c2(c+1)(c−1)2},DIV:={(a,κ)∈U| a>(c+1)24}. |
Theorem 3.1 gives dynamical behaviors of system (4) near
p1:=−12{(a−b−c+1)−{(a−b−c+1)2−4(a−c)}1/2},p2:=−12{(a−b−c+1)+{(a−b−c+1)2−4(a−c)}1/2} |
as in [27].
Parameters |
Equilibria | Limit cycles and homoclinic orbits | Region in bifurcation diagram | |||
saddle | unstable focus | saddle | ||||
saddle | unstable focus | saddle | one homoclinic rrbit | |||
saddle | unstable focus | saddle | one limit cycle | |||
saddle | stable focus | saddle | ||||
saddle | stable focus | saddle | ||||
saddle-node | ||||||
cusp | ||||||
saddle-node |
The appearance of limit cycle displays a rise of oscillatory phenomenon in system (4). Choosing parameters
In this paper we only considered parameters in
The functions in system (15) are
E00:={(2p+2)c2−(p2+3p)c−2p3}4ε1/{c2(p+1)p6(c−p)4},E10:=−{(2p+2)c2−(p2+3p)c−2p3}2ε1{(−6c3p−4c3p2−4p3c2+3p2c2+4cp4+4c4p+3c4)−(p2c2−3c3p−3c2p+cp2+2cp3−2p4)ε1−(p3c2−2cp4+p5+4c2p4−5p5c−p3c3+2p6)ε2}/{(p+1)p4c3(c−p)4},E20:={(−2c6(p+1)2(c−p)2)+(9c3p2+4c2p4−13c4p+4p5c2+6p3c3+9c5p−15p2c4−2p4c3+4p2c5−4p3c4+6c5)ε1−(2p7c−6p7c2−6p6c2−2p5c4+6p6c3+2cp8−2p4c4+6p5c3)ε2+(6p5c2−2p4c3−6p6c−6p7c+6p6c2−2p5c3+2p7+2p8)ε1ε2+(6p3c3−4p2c4−2c2p4−10p3c2−9c4p−2cp4+17c3p2−2p5c+13c3p−9p2c2−6c4)ε21}/{2c3p2(c−p)2(p+1)},E01:=−{(2p+2)c2−(p2+3p)c−2p3}{2c3ε1+(cp4−2p3c2+c3p2)ε2+(2p4−6cp3+4p2c2)ε1ε2+(12c2−6cp)ε21}/{p2(c−p)2c3},E11:={(3c3p2−8p2c4−p4c3+2c5+2c2p4+4c5p+2p2c5−5c4p+2p5c2+2p3c3−3p3c4)+(3c2p4+3c3p+p2c2+2p5c+3p2c4+3p3c2+2c4p+2cp4−4p3c3+c3p2)ε1+(5p6c2−2p7c−3p6c+7p5c2+2c2p4−p5c−5p4c3−p3c3+p3c4+p4c4−4p5c3)ε2−(5p6c−4p5c2+p4c3−5c2p4−p3c2+7p5c+p3c3+2cp4−2p7−p5−3p6)ε1ε2+(13cp2−8cp4+9c3p2−38p2c2+5cp3+10p4+10p5+19c3p+10c3−13p3c2−25c2p)ε21}/{c2(p+1)(−p+c)},E02:={(c−2p−1)+(5c3−2c2p)ε1−(3p3c2−2c3p2−cp4)ε2+(p4−cp3)ε1ε2−(2cp−c2)ε21}/{(p+1)2(c−p)2}. |
The functions below system (18) are
ϕ11:=24c6p5+4c8p2−16c7p4+4c8p3−16c5p6+4p7c4+24c6p4−16c7p3−16c5p5+4c4p6+(9p4c4−16p6c3+40c3p7+68p5c4−26p3c5+3c8−6c8p+42c6p3+36c6p4−94c5p4+6c7p2−4c4p6−16c2p8−56c5p5−8c8p2+8c7p3+28c6p2−14c7p)ε1+(4c7p4+40c5p7−4c2p9−4c2p10+20c3p8+20c3p9−20c6p5+40c5p6−20c6p6−40c4p8+4c7p5−40p7c4)ε2−(40p2c5+12p4c3+32c7p2+8p5c3+12c7+92p3c5+8p6c2−32p3c4−12p6c3−28p7c2+4c5p4−88c6p2−56p4c4+36c7p+48p5c4−60c6p3+16cp8−32c6p)ε21+(12cp9−24p7c4−8c7p4−6c7p3−88c5p5−32c2p8+20cp10−24c5p4+6c6p3+2c3p7−24p6c3+36c6p5+72c4p6+96c3p8−76c2p9+36p5c4+40c6p4−44c5p6+6p7c2)ε1ε2+(8p7c−9p2c4−16p5c2+6p3c3−c2p4+11p4c4+6p3c5+10p4c3−16p5c3−18p2c5+12p3c4−9c6p2+4p6c−4p8)ε31+(−34c4p6+2c3p7+4p9−28cp9−16cp8+6p4c4+8p10−2p7c+32c2p8+32p7c2−32p6c3−14p5c3+10p6c2−6c6p4+26c5p5+12p5c4)ε21ε2+(4c3p7−c6p6+44c3p9−41c2p10−c4p6+4cp9+2c5p6−4p11+28c3p8−32c2p9+8c5p7−26c4p8+20p11c−12p7c4−p10−4p12+18cp10−6c2p8)ε1ε22,ϕ12:=(2p5c3−4p3c4+2p4c3+2p3c5+2p2c5−4p4c4)+(9c3p2+4c2p4−13c4p+4p5c2+6p3c3+9c5p−15p2c4−2p4c3+4p2c5−4p3c4+6c5)ε1+(−2p7c+6p7c2+6p6c2+2p5c4−6p6c3−2cp8+2p4c4−6p5c3)ε2+(6p3c3−4p2c4−2c2p4−10p3c2−9c4p−2cp4+17c3p2−2p5c+13c3p−9p2c2−6c4)ε21+(6p5c2−2p4c3−6p6c−6p7c+6p6c2−2p5c3+2p7+2p8)ε1ε2,ϕ21:=(6c10+12c8p5+69c8p4−77c9p3+20c7p6+9c6p4−33c7p3+18c5p6−34c6p5+45c8p2−26c7p4+102c8p3−27c9p−80c9p2+27c7p5+6c5p7+8c4p8−12c5p8−55c6p6−12c6p7+8c4p9+22c10p2−24c9p4+8c10p3+20c10p)ε1+(4p10c4+20p9c6−10p10c5−4p5c9+2p11c4−2c9p4−2p6c9+10p7c8+20p6c8+10c8p5−20p9c5+2c4p9−40p7c7−20c7p8−10c5p8−20c7p6+40c6p8+20c6p7)ε2+(−12c9+12c3p9−47c8p4+10c9p3−86c6p4−19c7p3+102c5p6−220c6p5+60c8p2+159c7p4−40c8p3+61c5p5+2c4p6−16p7c4−18c9p+3c9p2+92c7p5+12c3p8+26c5p7−14c4p8+53c8p+35c6p3−76c7p2−79c6p6)ε21+(2p5c9−34c8p5+2c3p9+19c8p4−10c9p3+151c7p6−17c5p6+39c6p5−45c7p4+26c8p3−2c3p10+3p7c4−6c9p2+23c7p5+77c5p7−26c4p8+145c5p8−85c6p6−227c6p7−31c4p9−2c9p4−103c6p8+83p7c7+51p9c5−2p10c4−4p11c3−27p6c8)ε1ε2+(−4p7c8−2p6c8−30p10c4−60p11c4+40p9c5−2p8c8+12p11c3+24c7p8+12c7p9−60p9c6+12p13c3−30p10c6+40p11c5−30p12c4−4p13c2−2p14c2−2p12c2+24p12c3−30c6p8+80p10c5+12p7c7)ε22+(−30c8+69p3c5−16p4c4−212p5c4+58p6c3+331c5p4−232c6p4+79c7p3+117c5p6−65c6p5−21c8p2−3c7p4+5c8p3+379c5p5−187c4p6+4p7c4−94c3p8+44c2p8+44c2p9+91c7p−53c8p−263c6p3+163c7p2−106c6p2−38c3p7)ε31+(36c2p10−10c8p5−199c3p9−41c8p4+9c7p6+18c2p11−166c6p4+84c7p3+165c5p6−297c6p5−18c8p2+193c7p4−48c8p3−110c3p10+164c5p5−76c4p6+47p7c4+123c7p5−78c3p8+18c2p9−208c5p7+351c4p8−219c5p8−62c6p6+79c6p7+233c4p9+12c3p7)ε21ε2+(−2p14c+c2p10−4c3p9−8c7p6+21c2p11−2p12c−72c3p10−4p13c−4c5p7+6c4p8−102c5p8+c6p6+45c6p7+118c4p9+58p9c6−114p10c5+121p11c4+102c6p8−22p7c7−212p9c5+233p10c4−138p11c3+40p12c2−70p12c3+20p13c2−14c7p8+p7c8+p6c8)ε1ε22(−176p3c4+41p4c3+769p3c5+293p2c5−388p4c4+27p5c3+28p6c2−178p5c4−58p6c3+4p7c2+20cp8+20cp9+603c5p4−192c6p4+75c7p3+127c5p5+34c4p6+72c7−234c6p−24c2p8+210c7p−616c6p3+213c7p2−658c6p2−44c3p7)ε41+(−286p5c4+154p6c3−32p7c2+136c2p10−56cp10−198c3p9−24cp9+262c5p4−32cp11−330c6p4+70c7p3+438c5p6−284c6p5+68c7p4+636c5p5−580c4p6−210p7c4+22c7p5−154c3p8+30c2p8+198c2p9+64c5p7+84c4p8−122c6p3+24c7p2−76c6p6+198c3p7)ε31ε2+(4p12+102c2p10−cp10−158c3p9−c7p6+198c2p11−64p12c−33cp11−c5p6−313c3p10−32p13c+4p7c4+8p13−6c3p8+4c2p9−57c5p7+132c4p8−126c5p8+10c6p6+26c6p7+272c4p9+4p14+16c6p8−p7c7−70p9c5+144p10c4−161p11c3+100p12c2)ε21ε22. |
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Parameters |
Equilibria | Limit cycles and homoclinic orbits | Region in bifurcation diagram | |||
saddle | unstable focus | saddle | ||||
saddle | unstable focus | saddle | one homoclinic rrbit | |||
saddle | unstable focus | saddle | one limit cycle | |||
saddle | stable focus | saddle | ||||
saddle | stable focus | saddle | ||||
saddle-node | ||||||
cusp | ||||||
saddle-node |
Parameters |
Equilibria | Limit cycles and homoclinic orbits | Region in bifurcation diagram | |||
saddle | unstable focus | saddle | ||||
saddle | unstable focus | saddle | one homoclinic rrbit | |||
saddle | unstable focus | saddle | one limit cycle | |||
saddle | stable focus | saddle | ||||
saddle | stable focus | saddle | ||||
saddle-node | ||||||
cusp | ||||||
saddle-node |